Methods and systems for identifying non-penetrating brain injuries

ABSTRACT

The present disclosure provides methods for identifying non-penetrating brain injury in a subject, as well as methods for classifying a subject that received a hit to the body that transmitted an impulsive force to the brain as either having a non-penetrating brain injury or not, by analyzing one or more components of frequency-following response (FFR) following administration of an acoustic stimulus to the subject. In addition, the present disclosure provides methods for assessing a subject&#39;s recovery from a non-penetrating brain injury. Also disclosed herein are processes and systems for automatically generating acoustic stimuli and processing brain response data to identify non-penetrating brain injuries in subjects.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the priority of U.S. provisional application62/291,206, filed Feb. 4, 2016, which is hereby incorporated byreference in its entirety.

FIELD OF THE INVENTION

The present disclosure provides methods for identifying non-penetratingbrain injury in a subject, as well as methods for classifying a subjectthat received a hit to the body that transmitted an impulsive force tothe brain as either having a non-penetrating brain injury or not, byanalyzing one or more components of frequency-following response (FFR)following administration of an acoustic stimulus to the subject. Inaddition, the present disclosure provides methods for assessing asubject's recovery from a non-penetrating brain injury. Also disclosedherein are processes and systems for automatically generating acousticstimuli and processing brain response data to identify non-penetratingbrain injuries in subjects.

BACKGROUND OF THE INVENTION

Non-penetrating brain injuries carry devastating potential forcognitive, neurologic, and socioemotional disease, but no currentapproach reliably identifies this type of brain injury or its severity.For example, the current standard for concussion diagnosis is largelysubjective in that it relies on accurate symptom reporting by thepatient. Thus, there are ongoing efforts to identify objective markersto assist in diagnosing a concussion and predicting recovery.

One area of focus is on cerebrospinal fluid- and blood-based biomarkersthat test for sequelae of neural injury. However, these biomarkers areinvasive and may not extend to milder forms of non-penetrating braininjury, such as concussions. A second area tries to adopt neuroimagingtechniques, such as diffusion tensor imaging and functional magneticresonance imaging, to detect concussions. However, these approaches relyon expensive equipment and contradictory results are often reported: forexample, both increases and decreases in white matter volume have beenassociated with mild traumatic brain injury. Visual, auditory, andsomatosensory evoked potentials have all been explored in individualsfollowing head injury, but contradictory findings have been reported.(Folmer, et al. Int. J. Psychophysiol. 82, 4-15 (2011); Munjal, et al.J. Trauma Acute Care Surg. 68, 13-18 (2010); Gosselin, N. et al.Neurosurg. Focus 33, E7 (2012)) Overall, current neuroimaging andelectrophysiological approaches for various forms of non-penetratingbrain injury show group differences but overlap between groupspotentially thwarts evaluation of individual differences. Thelimitations of the aforementioned approaches necessitate a freshmethodology that has granularity into the biological minutiae of soundprocessing, and one that reliably indicates individual differences.

BRIEF DESCRIPTION OF THE FIGURES

The application file contains at least one photograph executed in color.Copies of this patent application publication with color photographswill be provided by the Office upon request and payment of the necessaryfee.

FIG. 1 is a time-domain representation of an acoustic stimulusconsisting of a 40 msec /da/ syllable (top) and a brain's response tothe stimulus (bottom). The brain response to /da/ includes bothtransient and sustained response features. The /da/ syllable evokesseven characteristic response peaks that are termed V, A, C, D, E, F,and O. As can be seen in this figure, these peaks relate to majoracoustic landmarks in the stimulus. Peaks in the recorded brain responseoccur 7 to 8 msec after the corresponding stimulus landmark, which isconsistent with the neural transmission delay. In this figure, thestimulus waveform is shifted in time to account for this transmissiondelay and to maximize the visual coherence between the two signals. TheV-A complex in the brain response to /da/ is often referred to as theonset response. This sharp onset response arises from the broadband stopburst associated with /d/. Along with V and A, C and O are consideredtransient responses because they correspond to transient stimulusfeatures, the beginning and end of voicing, respectively. In thisfigure, the region bounded by D and F forms the frequency followingresponse. Peaks D, E, and F and the small voltage fluctuations betweenthem correspond to sustained stimulus features, namely the fundamentalfrequency (F₀) and its harmonics within the consonant-vowel formanttransition. The D-E and E-F interpeak intervals (8 to 9 msec duration,arrows) occur at the period of the F₀ of the stimulus, which ramps from103 to 125 Hz. A systematic approach for identifying these peaks hasbeen established and normative data for 3- to 4-yr olds, 5- to 12-yrolds, and young adults has been published. See, for example, Johnson, etal. (2008), Clin Neurophysiol, 119, 2623-2635; or Dhar, S., Abel, R.,Hornickel, J., et al. (2009), Clin Neurophysiol, 120, 959-966; or Skoeet al. (2014), Cerebral Cortex, 25, 1415-1426. Here, the stimulus plotis scaled to match the size of the response. Hence, the microvolt barrefers only to the response.

FIG. 2A-D depicts graphs showing that the neural coding of thefundamental frequency (F₀, peak at around 100 Hz), but not harmonic cues(peaks from 200 to 1000 Hz), is impaired in children with a concussion.(A,B) The concussed children (red) have smaller responses to the pitchof a talker's voice than their non-concussed peers (black). A regressionpredicting symptom load from neural processing of the F₀ (controllingfor sex) illustrates a high degree of similarity between reported andpredicted symptoms (C) and the majority of children in the concussiongroup are at or below the 50th percentile (D) relative to establishednorms. (B: Error bars represent±1 S.E.M.; D: Horizontal solid linesrepresent±1 SD of normative data, horizontal dashed lines represent±1.5SDs of normative data, and horizontal dotted lines represent 2 SDs ofnormative data).

FIG. 3A-B depicts graphs showing children with a concussion have smallerand slower neural response to speech. Comparison of the grand averagebrain response (A) for the concussion (red) and control groups (black).Brain responses of concussed children are smaller over theconsonant-vowel transition (A,B) and slower (A) than those of theirnon-concussed peers. Error bars represent ±1 S.E.M.

FIG. 4A-C depicts graphs showing the longitudinal evidence that F₀processing improves as concussion symptoms abate. Between Test 1 andTest 2 (burgundy and red lines, respectively) the amplitude of responsesto the F₀ increases (A). The mean (±1 S.E.M.) of the concussion group atboth test points. On average, they no longer differ from the controlgroup with respect to F₀ processing (mean±1 S.E.M. showed as gray shadedregion) (B). Although 5 of the subjects are within this range, 6 showincreases beyond that range. Changes in F₀ amplitude for individualsubjects from the concussion group are shown (C). The shaded gray areashows the range F₀ amplitude that would indicate chance level of changebased on normative data.

FIG. 5A-B show graphs depicting neural responses in the frequencydomain. (A) Student-athletes with one prior concussion (blue) havesmaller F₀ responses (at 100 Hz) than their teammates who have never hada concussion (black). The two groups have similar responses to thehigher-frequency harmonics, however. (B) Percentiles are shown for F₀responses referenced to published norms. Student-athletes, on average,perform around the 40^(th) percentile. Those with a previous concussion,however, perform around the 20^(th) percentile. Shaded regions and errorbars indicate 1 SEM.

FIG. 6 illustrates an example process 600 for generating stimuli andprocessing brain response data to identify or otherwise determinenon-penetrating brain injuries.

FIG. 7 illustrates an example of a computing environment and/orcomputing system 700 that automatically transmits acoustic stimuli,receives and processes brain response data, and automatically generatesindications of non-penetrating brain injuries based on the brainresponse data.

FIG. 8 illustrates an example process 800 for generating stimuli andprocessing brain stem response data to identify or otherwise determinenon-penetrating brain injuries.

FIG. 9 illustrates an example of a suitable computing and networkingenvironment 900 that may be used to implement various aspects of thepresent disclosure described in FIGS. 6 and 7 (e.g. the computing device702 and corresponding components).

FIG. 10A-B depicts graphs showing that the metrics put into the logisticregression model are more accurate identifying groups in aggregate thanindividually. (A) Receiver operating characteristic (ROC) curves for themodel in Example 6 (red) and each of the scores that went into thatmodel. These plots show the tradeoff in sensitivity (true positive rate)and specificity (true negative rate) for each score. For example, as canbe seen in the red line a 90% true positive rate for the modelcorresponds to a 95% true negative rate. Black—subject age in years;Yellow—the amplitude of the prestimulus region of the FFR; Gray—thelatency of Wave V of the click-evoked auditory brainstem response;Green—the area of the onset portion of the FFR; Blue—F₀ amplitude;Cyan—stimulus-response correlation. (B) Area under the curve (AUC) ofeach ROC curve are graphed, with colors as described in (A). Higher AUCscorrespond to more accurate models. The error bars show the 95%confidence intervals for each of those ROC lines.

FIG. 11 shows a representative subject's FFR 48 hours after sustaining aconcussion (top) and after recovery (bottom). This analysis uses the“phase consistency” approach to quantifying the F₀. These figures are 3Dplots of the FFR. The x-axis is the time point in the response, they-axis in the frequency point in the response, and the colorscale showsthe phase consistency (strength) of each time-frequency point in thatplot. Red is the strongest phase consistency and so is best. The F₀ ofthis response is at 100 Hz. After recovery (bottom), when subject wascleared to play, there is a stronger response in that frequency band ascompared to 48 hours after sustaining the concussion (top). This figureillustrates (1) using a longer speech stimulus (i.e., 170 msec /da/syllable) with the phase consistency metric to compute the strength ofcoding the F₀, and (2) following an individual over time and seeing howthe response improves through recovery.

SUMMARY OF THE INVENTION

One aspect of the invention encompasses methods for identifyingnon-penetrating brain injury in a subject, the method comprisinganalyzing one or more components of a subject's frequency followingresponse (FFR) to an acoustic stimulus comprising a complex sound; andidentifying the subject as having a non-penetrating brain injury when avalue for at least one component of the brain response is anomalous. Themethod may further comprise analyzing one or more transient responses toan acoustic stimulus.

Another aspect of the invention encompasses methods for identifyingnon-penetrating brain injury in a subject, the method comprisinganalyzing one or more components of a subject's frequency followingresponse (FFR) to an acoustic stimulus comprising a complex sound, andidentifying the subject as having a non-penetrating brain injury when avalue for at least one component of the brain response is anomalous,wherein the component(s) are selected from the group consisting offundamental frequency (F₀) and/or harmonics, neural timing of a responsepeak, response amplitude over a time window that encompasses some or allof a sustained response, and stimulus-response correlation over a timewindow that encompasses some or all of a sustained response. The methodmay further comprise analyzing one or more transient responses to anacoustic stimulus.

Another aspect of the invention encompasses methods for identifyingnon-penetrating brain injury in a subject, the method comprising (a)fitting the subject with electrodes to measure voltage potentialsgenerated from the subject's brain; (b) administering to the subject anacoustic stimulus, wherein the acoustic stimulus is comprised of acomplex sound, and the complex sound comprises a consonant, aconsonant-to-vowel transition, and optionally a vowel; (c) recordingvoltage potentials from the subject's brain for at least the duration ofthe acoustic stimulus; (d) analyzing the voltage potentials to determineone or more components of the brain response; and (e) identifying thesubject as having a non-penetrating brain injury when a value for atleast one component of the brain response is anomalous; wherein thecomponents are selected from the group consisting of fundamentalfrequency (F₀), neural timing of a sustained response peak, responseamplitude over a time window that comprises some or all of theconsonant-vowel transition, and stimulus-response correlation over atime window that encompasses some or all of the consonant-voweltransition.

Another aspect of the invention encompasses methods for classifying asubject that received a hit to the body that transmitted an impulsiveforce to the brain as either having a non-penetrating brain injury ornot, the method comprising analyzing one or more components of asubject's frequency following response (FFR) to an acoustic stimuluscomprising a complex sound; and classifying the subject as having anon-penetrating brain injury when a value for at least one component ofthe brain response is anomalous. The method may further compriseanalyzing one or more transient responses to an acoustic stimulus.

Another aspect of the invention encompasses methods for classifying asubject that received a hit to the body that transmitted an impulsiveforce to the brain as either having a non-penetrating brain injury ornot, the method comprising analyzing one or more components of asubject's frequency following response (FFR) to an acoustic stimuluscomprising a complex sound; and classifying the subject as having anon-penetrating brain injury when a value for at least one component ofthe brain response is anomalous, wherein the component(s) are selectedfrom the group consisting of fundamental frequency (F₀) and/orharmonics, neural timing of a response peak, response amplitude over atime window that encompasses some or all of a sustained response, andstimulus-response correlation over a time window that encompasses someor all of a sustained response.. The method may further compriseanalyzing one or more transient responses to an acoustic stimulus.

Another aspect of the invention encompasses methods for classifying asubject that received a hit to the body that transmitted an impulsiveforce to the brain, the method comprising (a) fitting the subject withelectrodes to measure voltage potentials generated from the subject'sbrain; (b) administering to the subject an acoustic stimulus, whereinthe acoustic stimulus is comprised of a complex sound, and the complexsound comprises a consonant, a consonant-to-vowel transition, andoptionally a vowel; (c) recording voltage potentials from the subject'sbrain for at least the duration of the acoustic stimulus; (d) analyzingthe voltage potentials to determine one or more components of the brainresponse; and (e) classifying the subject as having a non-penetratingbrain injury when a value for at least one component of the brainresponse is anomalous; wherein the components are selected from thegroup consisting of fundamental frequency (F₀), neural timing of asustained response peak, response amplitude over a time window thatcomprises some or all of a consonant-vowel transition, andstimulus-response correlation over a time window that encompasses someor all of a consonant-vowel transition.

Another aspect of the invention encompasses method for assessing achange in a non-penetrating brain injury, the method comprises (a)analyzing one or more components of a subject's FFR to an acousticstimulus comprising a complex sound; (b) re-testing the subject's FFR tothe acoustic stimulus at a later time; and determining any differencesin the one or more components from step (a). If the absolute value ofthe difference is greater than would be expected by chance, there is achange in the non-penetrating brain injury

Another aspect of the invention encompasses methods for assessing asubject's recovery from a non-penetrating brain injury, the methodcomprising (a) analyzing one or more components of a subject's brainresponse to an acoustic stimulus comprising a complex sound; (b)re-testing the subject's brain response to the acoustic stimulus at alater time; and determining any differences in the one or morecomponents from step (a); wherein the component(s) is selected from thegroup consisting of fundamental frequency (F₀) and/or harmonics, neuraltiming of a response peak, response amplitude over a time window thatencompasses some or all of a sustained response, and stimulus-responsecorrelation over a time window that encompasses some or all of asustained response. If the absolute value of the difference is greaterthan would be expected by chance, there is a change in thenon-penetrating brain injury. The direction of the change indicatesimprovement or worsening/deterioration.

Another aspect of the invention encompasses method for assessing asubject's recovery from a non-penetrating brain injury, the methodcomprises two steps. The first step comprises (a) testing the subject'sbrain response to an acoustic stimulus by: (i) fitting the subject withelectrodes to measure voltage potentials generated from the subject'sbrain; (ii) administering to the subject an acoustic stimulus, whereinthe acoustic stimulus is comprised of a complex sound, and the complexsound comprises a consonant, a consonant-to-vowel transition, andoptionally a vowel; (iii) recording voltage potentials from thesubject's brain for at least the duration of the acoustic stimulus; (iv)analyzing the voltage potentials to determine one or more components ofthe brain response; and (v) identifying a value for at least onecomponent of the brain response that is anomalous; wherein thecomponents are selected from the group consisting of fundamentalfrequency (F₀), neural timing of a sustained response peak, responseamplitude over a time window that comprises some or all of theconsonant-vowel transition, and stimulus-response correlation over atime window that encompasses some or all of the consonant-voweltransition. The second step comprises (b) re-testing the subject's brainresponse to the acoustic stimulus at a later time by repeating stepsa(i) to a(iv), and identifying the value for the one or more componentsthat were anomalous in step (a)(v) (“the re-test value”); and (c)calculating the difference between the re-test value and the anomalousvalue. The subject is determined to be recovering from thenon-penetrating brain injury when there is a change in the re-test valuethat is greater than would be expected by chance, and the direction ofthe change indicates an improvement in the component of the brainresponse. The subject is determined to not be recovering from thenon-penetrating brain injury when (a) there is not a change in there-test value that is greater than would be expected by chance, or (b)when there is a change in the re-test value that is greater than wouldbe expected by chance, and the direction of the change indicates adeterioration in the component of the brain response.

Other features and aspects of the invention are described in more detailherein.

DETAILED DESCRIPTION OF THE INVENTION

Applicants have discovered that non-penetrating brain injury can beidentified in a subject by analyzing one or more components offrequency-following response (FFR) following administration of anacoustic stimulus to the subject. The FFR reflects sustained neuralactivity over a population of neural elements. Various aspects of theFFR as described in further detail below.

As used herein, the term “non-penetrating brain injury” refers to a typeof brain injury caused by an indirect or a direct hit to a subject'sbody that transmits an impulsive force to the subject's brain. Theinjury may occur after a single blow or after repeated blows. Theindirect or direct hit can be to the head, the neck, or elsewhere on thebody. Non-limiting examples of indirect hits to the body that may resultin non-penetrating brain injury include whiplash, a blast wave from anexplosion, or other acceleration or deceleration forces on the body.Non-limiting examples of direct hits to the body that may result innon-penetrating brain injury include head-to-head contact, head-to-otherbody part (hand, foot, leg, elbow, shoulder, etc.) contact,head-to-ground contact, head-to-object contact (sports equipment (e.g.ball, puck, stick, sword, surfboard, ski, etc.), moving objects,stationary objects, etc.), etc. Various types of “non-penetrating braininjury” include, but are not limited to, concussions and traumatic braininjury (e.g., mild, moderate, severe, etc.). A subject that has anon-penetrating brain injury may or may not have detectable signs ofphysical brain injury or symptoms commonly associated therewith. Theterm “non-penetrating brain injury” excludes penetrating brain injuries.A penetrating brain injury is a head injury in which the dura mater isbreached. The term “non-penetrating brain injury” also excludesbiological insults to the brain, e.g., protein aggregate diseases (e.g.,Alzheimer's disease, Parkinson's disease, Huntington's disease,amyotrophic lateral sclerosis, prion diseases, etc.), demyelinatingdiseases (e.g., Multiple Sclerosis, Devic's disease, Vitamin B12deficiency, etc.) bacterial infections, encephalitis, tumors, etc.

A subject of this disclosure is a human or an animal. Suitable subjectsinclude a human, a livestock animal, a companion animal, a laboratoryanimal, and a zoological animal. In a preferred embodiment, a subject ishuman. Also contemplated are subjects that have an increased risk ofnon-penetrating brain injury, including, but not limited to, humansubjects that are, or were, athletes (amateur or professional),soldiers, victims of physical abuse, involved in a motor vehiclecollision, or involved in a bicycle or pedestrian accident, as well assubjects that had a previous non-penetrating brain injury. Methods ofthis disclosure may not be suitable for subjects with deafness or knownneurological conditions which may have an impact on FFR may be excluded(e.g. multiple sclerosis, epilepsy.)

The present disclosure provides methods for identifying non-penetratingbrain injury in an asymptomatic or a symptomatic subject, as well asmethods for assessing a subject's recovery from a non-penetrating braininjury. Also disclosed herein are processes and systems forautomatically generating acoustic stimuli and processing brain responsedata to identify non-penetrating brain injuries in subjects. Variousaspects of this discovery are described in further detail below.

I. Evoking a Brain Response to a Complex Sound

A brain response to sound is evoked by presenting an acoustic stimuluscomprising a complex sound to a subject. The brain's response to theacoustic stimulus can be recorded in a number of different ways. In thepresent disclosure, the brain's response is measured using electrodesthat pick up electrical potentials generated by populations of neuronsin the brain. An “acoustic stimulus,” as used herein, is an input of oneor more sounds. A “complex sound” is a sound comprised of two or morefrequencies. The term “brain response” refers to a recorded measurementof the voltage potentials from a subject's brain evoked by an acousticstimulus comprising a complex sound. An acoustic stimulus may bepresented once or multiple times. Each presentation of the same acousticstimulus may be referred to as a “trial.” In embodiments where anacoustic stimulus is presented multiple times, the temporal intervalbetween the offset of one stimulus to the onset of another can vary suchthat there is no amount of time between the stimuli or various amountsof time are included. This interval is referred to as the interstimulusinterval. A non-limiting example of a range for an interstimulusinterval may be zero msec to about 80 msec. Considerations for choosingan appropriate interstimulus interval are known in the art. See, forexample, Skoe et al., Ear & Hearing, 2010, 31(3) and the referencesdisclosed therein.

(a) Acoustic Stimulus

An acoustic stimulus comprises a complex sound and, optionally,background noise.

i. Complex Sound

A complex sound is a sound comprised of two or more frequencies. Theterm complex sound includes amplitude, frequency, or phase modulatedwaves. An amplitude modulated wave is when the amplitude of a carrierwave, such as a sine wave, is altered by a modulating wave. For example,a 1000 Hz sine wave carrier could be modulated by a 300 Hz sine wavetone. These waves do not have to be tones. Similarly, a wave can also bemodulated in frequency or phase. The term “complex sound” excludessimple sounds known in the art including, but not limited to, clicks andsinusoidal tones that are not modified. A complex sound may be natural,synthetic, or a hybrid. Minimally, a complex sound used in the methodsof this disclosure should elicit a clear and reproducible brain responsein healthy subjects. Synthetic or hybrid sounds are preferred becausethey offer precise control over the various aspects of sound butwell-characterized audio files of natural sounds are suitable as well.Non-limiting examples of complex sounds include vocal sounds,environmental sounds, and musical sounds. Vocal sounds include, but arenot limited to, a speech syllable, a word, and a non-speech vocal sound(e.g., a cry, a grunt, an animal sound, etc.). Musical sounds include,but are not limited to a note played by an instrument, a consonanttwo-note interval played by an instrument, a dissonant two-note intervalplayed by an instrument, and a musical chord. Environmental soundsinclude, but are not limited to a rainfall sound, an ocean sound, a carhorn, a train whistle, etc.

Complex sounds used in the present disclosure have aspects that maximizetransient and sustained brain responses. In one aspect, a complex soundhas one or more strong transient features. Transient features are briefand nonsustained, and evoke fast response peaks lasting fractions ofmillisecond (i.e., a transient brain response). The relative strength ofa transient feature refers to the timing and/or amplitude. The onset ofsound and the offset of sound are common transient features of complexsound. The onset of sound is also referred to as “attack,” which is theamount of time taken for the amplitude to reach its maximum level. Theoffset of sound is also referred to as “release,” which is the finalreduction in amplitude over time. A transient feature may also be an“amplitude burst,” which is an abrupt change in the amplitude envelopeof a complex sound. For example, a baby's cry can include multipleamplitude-bursts that produce a series of sharp, transient responses.

For a given group of complex sounds, the strength of a transient featurecan be determined by one of skill in the art through routineexperimentation, or may be known in the art. For example, among speechsounds, obstruent stop consonants (e.g., /d/, /p/, /k/, /t/, /b/, /g/,etc.) have faster and steeper onsets than affricate consonants (e.g.,

and

, etc.), which have faster and steeper onsets than fricative consonants(e.g., /z/, etc.), which have faster and steeper onsets than sonorantconsonants (e.g. nasals, glides, and slides (e.g., /r/, /l/, etc.).Similarly, musical sounds have varying attack properties that depend onthe instrument and how the instrument is played. For example, percussiveinstruments have fast, steep attacks, and bowed string instruments havecomparatively smoother attacks; and a plucked string has a shorter risetime than a bowed string.

In another aspect, a complex sound has a fundamental frequency (F₀) inthe range of about 50 Hz to about 500 Hz. Fundamental frequencies withinthis range elicit a strong (i.e., sustained), phase-locked brainresponse to the F₀ and its harmonics. Because phase-locking may becomeweaker with increasing frequency, a F₀ range of about 50 Hz to about 400Hz may be preferred. Alternatively, the F₀ range may be a range about 80Hz to about 400 Hz, or about 80 Hz to about 300 Hz. In some embodiments,a complex sound may have an F₀ that is stable. In some embodiments, acomplex sound may have an F₀ that changes. In other embodiments, thestimulus may be manipulated to remove the F₀ and only contain theharmonic integer frequencies of the F₀. In this instance a listenerstill perceives a fundamental frequency that is approximated as thecommon denominator from the harmonics. For example, a harmonic series at200, 300, 400, and 500 Hz would result in a perceived F₀ at 100 Hz, andthere would be a brain response at 100 Hz.

In embodiments where the complex sound is a speech sound, voicedportion(s) of the sound provide the sustained features. Many, but notall, consonants sounds are unvoiced, meaning that the vocal cords arenot in motion. In most languages all vowels are voiced, meaning that thevocal cords are in motion. Thus, a “consonant-to-vowel transition” ofteninvolves a change, acoustically, from an unvoiced speech segment to avoiced speech segment. Non-limiting examples of a voiced portion of asound include a consonant-to-vowel transition, a voiced consonanttransition, or a steady-state vowel portion. Though non-speech vocalsounds from animals do not include consonants and vowel, they do containvoiced sounds (for those animals with vocal cords) and other soundsfiltered by the vocal tract. As such, non-speech vocal sounds containacoustic features that are substantially similar to a consonant-to-voweltransition in a speech sound.

The duration of a complex sound can vary. The minimum duration is atleast one cycle of the complex sound's F₀. For example, the duration maybe 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more cycles of the complex sound'sF₀. A skilled artisan can determine an appropriate number of cycles byroutine experimentation, or based on teachings in the art. For example,the prior art teaches that musical timbre and vowel identity can beaccurately determined from one to four cycles of the F₀ but pitchidentification typically requires about four or more cycles. See, forexample, Gray, G. W. (1942), Speech Monographs, 9, 75; or Robinson, K.(1995), Music Perception, 13, 1-15; or Robinson, K., & Patterson, R. D.(1995), J Acoust Soc Am, 98, 1858-1865. Generally speaking, the onlyfactor limiting the duration of an acoustic stimulus is the feasibilityof having a subject remain still for a long time. Thus, duration mayneed to be restricted to present the desired number of acoustic stimuliin a reasonable amount of time. In various embodiments, the duration maybe about 10 msec, about 20 msec, about 30 msec, about 40 msec, about 50msec, about 60 msec, about 70 msec, about 80 msec, about 90 msec, about100 msec, or more.

When an acoustic stimulus has a complex sound that is a speech syllable,one strategy to limit duration is to use a consonant and aconsonant-vowel (CV) transition without a steady-state vowel. See, forexample, Russo et al. (2004), Clin Neurophysiol, 115, 2021-2030;Johnson, et al. (2007), J Cogn Neurosci, 19, 376-385; Johnson, et al.(2008), J Neurosci, 28, 4000-4007; Hornickel, et al. (2009), AudiolNeurootol, 14, 198-207; Banai, et al. (2009), Cereb Cortex, 19,2699-2707; Dhar, et al. (2009), Clin Neurophysiol, 120, 959-966. Becauseeach consonant-to-vowel transition has a unique formant transition, thesteady state vowel can be removed with little impact on the percept.Within this disclosure, speech syllables with a consonant-to-voweltransition are identified by the consonant and the vowel, e.g., /da/,but this nomenclature is understood to include a consonant and aconsonant-vowel (CV) transition without a steady-state vowel.

ii. Background Noise

The term “background noise” refers to any sound that occurs at the sametime as the sound of interest, e.g., the complex sound intentionallyadministered to a subject to elicit an auditory response. Non-limitingexamples of “background noise” include white noise, pink noise, a murmurof voices, traffic, construction, etc.

iii. Stimulus Creation/Presentation

To elicit a brain response, an acoustic stimulus of the disclosure iscreated and then presented to a subject. Natural sounds are recorded andthen presented, and artificial sounds are synthesized and thenpresented. Various aspects of presentation including stimulus intensity,monoaural and binaural stimulation, left and right ear stimulation,stimulus polarity, stimulation rate, transducers, jitter in the stimuluspresentation, and multiple stimulus conditions are standard in the art.See, for example, Skoe et al., Ear & Hearing, 2010, 31(3) and thereferences disclosed therein. Example description of generating astimulus can also be found in U.S. Pat. Nos. 8,014,853; 8,712,514; and8,825,140, and U.S. 15/001,674 co-owned by the present applicant, eachof which is herein incorporated by reference in its entirety.

iv. Exemplary Embodiments

In some exemplary embodiments, a complex sound comprises a soundselected from the group consisting of an environmental sound, a musicalsound, a speech sound, and a non-speech vocal sound. The complex soundhas a fundamental frequency (F₀) that ranges from about 50 Hz to about500 Hz, and duration of at least about 10 msec. In certain embodiments,the fundamental frequency ranges from about 80 Hz to about 300 Hz and/orthe duration may be at least about 20 msec, at least about 30 msec, orat least about 40 msec. In certain embodiments, the fundamentalfrequency ranges from about 130 Hz to about 350 Hz and/or the durationmay be at least about 20 msec, at least about 30 msec, or at least about40 msec. In certain embodiments, the fundamental frequency ranges fromabout 180 Hz to about 400 Hz and/or the duration may be at least about20 msec, at least about 30 msec, or at least about 40 msec. In certainembodiments, the fundamental frequency ranges from about 230 Hz to about450 Hz and/or the duration may be at least about 20 msec, at least about30 msec, or at least about 40 msec. In certain embodiments, thefundamental frequency ranges from about 280 Hz to about 500 Hz and/orthe duration may be at least about 20 msec, at least about 30 msec, orat least about 40 msec.

In other exemplary embodiments, a complex sound comprises a speech soundor a non-speech vocal sound. In other exemplary embodiments, a complexsound consists of a speech sound or a non-speech vocal sound. Thecomplex sound has a fundamental frequency (F₀) that ranges from about 50Hz to about 500 Hz, and duration of at least about 10 msec. In certainembodiments, the fundamental frequency ranges from about 80 Hz to about300 Hz and/or the duration may be at least about 20 msec, at least about30 msec, or at least about 40 msec. In certain embodiments, the speechsound is speech syllable, non-limiting examples of suitable speechsyllables are listed in the table below.

Speech Syllable Examples Publications Vowels Synthetic /a/, /u/Krishnan, 2002 Natural /ε/, /i/, /V, /a/, /ae/, /{circumflex over ( )}/,/u/ Greenburg et al. 1980; Dajani et al. 2005, Aiken & Picton 2006, 2008Consonant-vowel syllables Synthetic /da/ Cunningham et al. 2001; Plyler& Ananthanarayan 2001; King et al. 2002; Wible et al. 2004, 2005; Russoet al. 2004, 2005; Kraus & Nicol 2005; Johnson et al. 2007, 2008; Banaiet al. 2005, 2009; Burne et al. 2009; Chandarasekaran et al. 2009;Parbery-Clark et al. 2009a /ba/ Akhoun et al. 2008a, b ba-da-gacontinuum Plyer & Ananthanarayan 2001; Johnson et al. 2008; Hornickel etal. 2009b Natural Mandarin pitch contours /yl/ Krishnan et al. 2005; Xuet al. 2006 /ml/ Wong et al. 2008; Song et al. 2008 Hybrid /ya/ withlinearly rising and falling Russo et al. 2008 pitch contours

In another exemplary embodiment, a complex sound has a duration of atleast about 10 msec and comprises a first sound that transitionsdirectly to a second sound, wherein the first sound has an attacksubstantially similar to an obstruent consonant and the second sound hasa minimum of two formants held steady for one period of F₀ and F₀ rangesfrom about 50 Hz to about 500 Hz. In some embodiments the complex soundis a musical sound. In other embodiments the complex sound is anenvironmental sound. In other embodiments the complex sound is vocalsound.

In another exemplary embodiment, a complex sound comprises a speechsyllable, the speech syllable comprising a consonant-vowel transition, adiphthong, a triphthong, or a linguistic pitch contour. The complexsound may or may not be a word. The complex sound has a fundamentalfrequency (F₀) that ranges from about 50 Hz to about 500 Hz, andduration of at least about 10 msec. In certain embodiments, thefundamental frequency ranges from about 80 Hz to about 300 Hz and/or theduration may be at least about 20 msec, at least about 30 msec, or atleast about 40 msec.

In another exemplary embodiment, a complex sound consists of a speechsyllable, the speech syllable comprising a consonant-vowel transition, adiphthong, or a linguistic pitch contour. The complex sound has afundamental frequency (F₀) that ranges from about 50 Hz to about 500 Hz,and duration of at least about 10 msec. In certain embodiments, thefundamental frequency ranges from about 80 Hz to about 300 Hz and/or theduration may be at least about 20 msec, at least about 30 msec, or atleast about 40 msec.

In other exemplary embodiments, a complex sound comprises a consonant, aconsonant-to-vowel transition, and optionally a vowel. The complex soundmay or may not be a word. The complex sound has a fundamental frequency(F₀) that ranges from about 50 Hz to about 500 Hz, and duration of atleast about 10 msec. In certain embodiments, the fundamental frequencyranges from about 80 Hz to about 300 Hz and/or the duration may be atleast about 20 msec, at least about 30 msec, or at least about 40 msec.Non-limiting examples include /da/, /po/, /chu/, /ki/, /yi/, and /mi/,and variations thereof where the consonants and vowels are substitutedfor other consonants and vowels that produce similar acoustic features.

In other exemplary embodiments, a complex sound consists of a consonant,a consonant-to-vowel transition, and optionally a vowel. The complexsound has a fundamental frequency (F₀) that ranges from about 50 Hz toabout 500 Hz, and duration of at least about 10 msec. In certainembodiments, the fundamental frequency ranges from about 80 Hz to about300 Hz and/or the duration may be at least about 20 msec, at least about30 msec, or at least about 40 msec. Non-limiting examples include /da/,/po/, /chu/, /ki/, /yi/, and /mi/, and variations thereof where theconsonants and vowels are substituted for other consonants and vowelsthat produce similar acoustic features.

In another exemplary embodiment, a complex sound has a duration of atleast about 10 msec and comprises a first sound that transitionsdirectly to a second sound, wherein the first sound is an obstruentconsonant and the second sound has a minimum of two formants held steadyfor one period of F₀ and F₀ ranges from about 50 Hz to about 500 Hz.

In another exemplary embodiment, a complex sound has a duration of atleast about 10 msec and consists of a first sound that transitionsdirectly to a second sound, wherein the first sound is an obstruentconsonant and the second sound has a minimum of two formants held steadyfor one period of F₀ and F₀ ranges from about 50 Hz to about 500 Hz.

In other exemplary embodiments, a complex sound comprises a consonant, aconsonant-to-vowel transition, and optionally a vowel, wherein theconsonant is an obstruent stop consonant and the vowel is a low backvowel. A non-limiting example of this complex sound is /da/. The complexsound may or may not be a word. The complex sound has a fundamentalfrequency (F₀) that ranges from about 50 Hz to about 500 Hz, andduration of at least about 10 msec. In certain embodiments, thefundamental frequency ranges from about 80 Hz to about 300 Hz and/or theduration may be at least about 20 msec, at least about 30 msec, or atleast about 40 msec.

In other exemplary embodiments, a complex sound consists of a consonant,a consonant-to-vowel transition, and optionally a vowel, wherein theconsonant is an obstruent stop consonant and the vowel is a low backvowel. A non-limiting example of this complex sound is /da/. The complexsound has a fundamental frequency (F₀) that ranges from about 50 Hz toabout 500 Hz, and duration of at least about 10 msec. In certainembodiments, the fundamental frequency ranges from about 80 Hz to about300 Hz and/or the duration may be at least about 20 msec, at least about30 msec, or at least about 40 msec.

In other exemplary embodiments, a complex sound comprises a speechsyllable selected from the group consisting of /da/, /pa/, /ka/, /ta/,/ba/, and /ga/. The complex sound may or may not be a word. The complexsound has a duration of at least about 10 msec. In certain embodiments,the duration may be at least about 20 msec, at least about 30 msec, orat least about 40 msec.

In other exemplary embodiments, a complex sound consists of a speechsyllable selected from the group consisting of /da/, /pa/, /ka/, /ta/,/ba/, and /ga/. The complex sound has a duration of at least about 10msec. In certain embodiments, the duration may be at least about 20msec, at least about 30 msec, or at least about 40 msec.

(b) Brain Response

Sound evokes a precise neural response in a subject's brain. In thepresent disclosure, a brain's response is measured using electrodes thatpick up electrical potentials generated by populations of neurons in thebrain. The term “brain response” refers to a recorded measurement of thevoltage potentials from a subject's brain evoked by an acoustic stimuluscomprising a complex sound. Various aspects relating to electrodeplacement, sampling rate, filtering, signal averaging, and minimizingartifacts can be optimized through routine experimentation. The tablebelow provides a general recommendation for some of these aspects. Forfurther detail, see for example, Skoe et al., Ear & Hearing, 2010, 31(3)and the references disclosed therein, as well as U.S. Pat. Nos.8,014,853; 8,712,514; and 8,825,140, and U.S. Ser. No. 15/001,674, eachhereby incorporated by reference in its entirety.

Parameter Recommendation Rationale/Comments Electrode placement Verticalmontage (active; Cz; reference; earlobe(s); For rostral brain stemrecordings; a horizontal montage ground; forehead) is used for recordingfrom more peripheral structures Sampling rate 6000-20000 Hz Bettertemporal precision with higher sampling rates Filtering Low-pass cutoff:2000-3000 Hz More defined transient peaks High-pass cutoff: 30-100 HzDepends on spectral characteristics of stimulus If possible, collectcABR with open filters (1-3000 Hz) Digital filters minimize temporalphase shifts and band-pass filter off-line using digital filters Signalaveraging 2 or more subaverages of 2000-3000 sweeps Determine responsereplicability Spectral-domain averaging will increase spectral estimatesand require fewer sweeps Averaging window Begin 10-50 msec beforestimulus onset An adequate sample of the baseline is needed to determinewhether a particular response peak is above the noise floor For runningwindow analysis, the pre-stimulus time window should be greater than orequal to the duration of the analysis window Extend 10-50 msec afterstimulus onset Neural activity should return to baseline SimultaneouscABR-cortical Only if large files can be accommodated and responserecording longer sessions are appropriate Minimizing artifacts Passivecollection protocol Minimizes myogenic artifacts Electromagneticshielded insert ear phones Minimize stimulus artifact Both stimuluspolarities Enables adding of responses to minimize both stimulusartifact and cochlear microphonic Use electrically shielded test boothMinimizes electrical artifact Project movie into test booth Artifactrejection criterion: >20 μV Exclude trials exceeding typical neuralresponse size; criterion depends on high-pass filter setting cABRs,auditory brain stem responses to complex sounds.

Generally speaking, a brain response consists of a plurality of positiveand negative amplitude deflections, referred to as “response peaks.” Abrain response is initially recorded as a series of voltages over time(referred to as the time domain response), which can be converted to aseries of voltages over frequency by methods well-known in the art(referred to as the frequency, or spectral, domain response). A brainresponse to complex sound contains multiple, independent, components inboth the time domain and the frequency domain. In the context ofidentifying non-penetrating brain injury, measurements of thesecomponents can be meaningful individually or in aggregate.

In the time domain, response peaks are generally classified as either atransient response peak or a sustained response peak. Similarly, regionsof the time domain containing transient response peaks or sustainedresponse peaks may be classified as a transient region or a sustainedregion, respectively. This terminology reflects a brain response toeither a transient feature or a sustained feature of a complex sound.The number and morphology of peaks in a brain response varies based onthe complex sound used. All sounds generate a response peakcorresponding to the onset of the sound (i.e., an onset peak), thoughthere is typically a lag of about 6 to about 10 msec between when asound begins and the onset peak. In some instances, a brain response tothe onset of sound is a biphasic peak (e.g., positive then negative ornegative then positive), rather than a single peak. Thepositive/negative pair may be referred to as an “onset response.” Thelag of about 6 to about 10 msec between the onset of sound and an onsetpeak is referred to as a “neural transmission delay” or a “neural onsetdelay.” An onset peak is a transient response. Additional transientresponses may also be present including, but not limited to, a brainresponse to the onset of an amplitude burst and a brain response to theoffset of sound. Complex sounds also generate response peaks that aretime-locked to the temporal structure of the eliciting sound. Theseresponse peaks are sustained features of a brain response and reflectsynchronous, population-wide neural phase locking. Sustained brainresponses are often called frequency following responses (FFR). Inembodiments where an acoustic stimulus includes an interstimulusinterval, the brain response will contain an interstimulus region.

The response peaks for complex sounds routinely used in the art arewell-known. For example, a 40 msec /da/ syllable produces sixstereotyped peaks: peak V, which is a positive amplitude deflectioncorresponding to the onset of the stimulus and occurring about 6 msec toabout 10 msec after stimulus onset; peak A, which is a negativeamplitude deflection immediately following peak A; peaks D, E, and F,which are negative amplitude deflections corresponding to the voicing ofthe speech sound and occurring at about 22 msec, about 32 msec, andabout 42 msec respectively; and peak O, which is a negative amplitudedeflection following the offset of the sound, occurring at about 50msec. A 170 msec /da/ syllable is described in White-Schwoch et al.Hearing Research 2015, 325:34-47, and descriptions of /ba/ and /ga/sounds may be found in Johnson et al. Clinical Neuropsychology119:2623-2635. The above description is not limiting. Additionalreferences are known in the art for other complex sounds. If a complexsound is novel, one of skill in the art can characterize the response bymethods known in the art.

Neural phase-locking is also evident in the frequency domain, where thebrain response follows the periodicity of the eliciting sound. As such,the F₀ and harmonics (i.e., integer multiples of F₀) of the elicitingsound are reflected in the brain response. Typically all harmonicspresent in an acoustic stimulus, up to the frequency limits that thebrain is able to produce, are present in a brain response. Though,generally speaking, phase locking is more robust when there is lessspectral flux (i.e., change in harmonics over time). Non-linearities ofthe auditory system will often result in additional harmonic peaks inthe response beyond those present in the stimulus.

When an acoustic stimulus contains a speech sound or a non-speech vocalsound, certain harmonics are of particular importance phonetically.These harmonics are called “formants.” Formants are harmonics that arelarger in amplitude than surrounding harmonics (both in the elicitingsound and the response). Each speech sound can be uniquely identified byits characteristic formant pattern, with the first two or three formantsbeing sufficient for identifying most speech sounds. For example, the/a/ sound will typically have a local peak at around 700-750 Hzregardless of the pitch (F₀) of the utterance. This is the first formantof /a/. The vowel /i/, on the other hand will have a first formant inthe 250-300 Hz range.

In contrast to speech, which is dominated by fast spectrotemporaltransitions, music has more prevailing temporal and spectral elements,slower transitions, and finer frequency spacing. In the same way thatspeech sounds are characterized by unique formant configurations,instruments also have characteristic harmonic structures that imparttimbre. Specifically, the timbre of a musical sound is determined by therise time of the attack, the spectral flux, and the spectral centroid(i.e., the distribution of the harmonics). The clarinet, for example,has a harmonic structure dominated by lower frequency odd harmonics (theeven harmonics have been attenuated). The flute, saxophone, trombone,and tuba, which are all characterized by strong odd and even harmonics,can be differentiated by the distribution of the harmonics (e.g., theenergy of the tuba is concentrated in the lower harmonics)

The spectral and temporal components of a brain response to complexsound have been described in detail elsewhere, as have methods tomeasure them. See, for example, Skoe et al., Ear & Hearing, 2010, 31(3)and the references disclosed therein. Certain aspects are describedbelow.

i. Brain Response Fundamental Frequency (F₀)

One aspect of a brain response to a complex sound is the extent to whichthe brain response reflects the F₀ of the stimulus. As describedelsewhere, F₀ is a defined parameter based on the acoustics of theeliciting sound. Various aspects of F₀ may be analyzed including but notlimited to, F₀ amplitude, F₀ sharpness, F₀ phase consistency, or pitchtracking.

To calculate response F₀, the time domain response must be converted toa frequency domain response. Suitable methods for achieving thisinclude, but are not limited to, fast Fourier transformation (FFT). FFTmay be computed on all or a portion of the time range collected. Thetime range over which the FFT is calculated may vary provided the range(1) accounts for a neural transmission delay, which is typically about6-10 msec or may alternatively be determined by the timing of the firstamplitude deflection in the brain response; (2) does not extend beyondthe end of the brain response, which is typically about 6-10 msec longerthan the length of the stimulus plus onset delay, and (3) includes onecycle of the period of the complex sound's F₀. For example, at least a10 msec time period is used to calculate the FFT for a complex soundwith an F₀ of 100 Hz (period is the inverse of frequency). The FFT maybe generated using any standard windowing approach known in the artincluding, but not limited to, a Hanning window, a Hamming window, aBlackman window, a cosine window, a Nuttall window, a Blackman-Harriswindow, and a flat-top window, etc. The length of the ramp in computingthe FFT can range from 1 msec up to half the length of the time windowover which the FFT is calculated. For example, if the FFT is calculatedover a 100 msec window, ramp times could include 1 msec, 2 msec, 3 msec,4 msec, 5 msec . . . up to 50 msec. The arithmetic mean of the amplitudeof the spectrum that corresponds to the F₀ of the complex sound iscalculated.

A response F₀ may be then determined by autocorrelation method. Anautocorrelation method is a standard procedure that time-shifts awaveform (A) with respect to a copy of itself (A′) and correlates A toA′ at many such time shifts. For example A(1:10) (i.e., points 1 to 10of waveform A), is correlated to A′(1:10), then A(1:10) is correlated toA′(2:11), then A′(3:12), etc. The reverse shift also is evaluated, suchat A(1:10) is correlated with A′(−1:9) and A′(−2:8) etc. Each time shiftis considered a “lag,” such that A(1:10) vs A′(1:10) has a lag of 0.A(1:10) vs A′(2:11) has a lag of 1, etc. The fundamental frequency ofthe waveform A will occur at 1/L_(max) Hz, where L_(max) is defined asthe lag (in sec) at which the maximum correlation is achieved. Thedefinition of L_(max) is further refined to exclude a lag of 0 whichwill always be the largest correlation. In practice, if there is a knownfrequency range of interest, it is possible to restrict the search forthe maximum correlation to lags that encompass the range of interest.For example, if a stimulus has a known F₀ of 100 Hz, one might wish torestrict the frequency range that is sought in the response to a rangeof 80 to 120 Hz. In this case, one would only look for the maximalcorrelation in a lag range of 1/80 sec to 1/120 sec (8.33 msec to 12.5msec). If a peak occurs at a lag of 9.7 msec, one would conclude thatthe response had an F₀ of about 103 Hz. Determining F₀ byautocorrelation method is particularly useful when the F₀ of theacoustic stimulus or brain response is not known a priori, when the F₀of the acoustic stimulus is known but one desires to determine at whatfrequency the response occurred, when an acoustic stimulus is missing afundamental type (e.g. the base frequency), or when a stimulus with aknown F₀ produces a response peak at a slightly different frequency.

Information known about the stimulus F₀ may also be used to choose asuitable frequency window for evaluating one or more aspects of F₀.Different frequency regions of the spectrum will be analyzed based onthe eliciting sound. In embodiments where the F₀ of the complex sound isstatic, the region may range from one-half the frequency of theeliciting sound F₀ (minimum of the region) to twice the frequency of theeliciting sound F₀ (maximum of the region). For example, for a complexsound with a 100 Hz F₀, the region of interest would be 50-200 Hz.Alternatively, a frequency window as small as 1 Hz may be selected. Inembodiments where F₀ of the complex sound varied, the parameters forselecting the F₀ analyses of the brain response may be determined by thearithmetic mean F₀ of the stimulus or the upper and lower bounds. Forexample, if the complex sound F₀ changed from 100-150 Hz the lower boundfrequency region of interest could extend as low as 50 Hz and the upperbound as high as 300 Hz.

One aspect of a response F₀ is the amplitude. Amplitude may becalculated over a frequency region that is about 1 Hz, or the frequencyregion may be a range greater than 1 Hz. Methods for calculatingresponse F₀ ranges that are greater than one are described above. Anysuitable method for quantifying F₀ amplitude may be used including, butnot limited to the arithmetic mean amplitude over a region, theamplitude at a single frequency point, the total amplitude over a regionof interest (summed, integer, root-mean-squared amplitude), and thesignal-to-noise ratio of the F₀ (i.e., amplitude of F₀ vs. amplitude ofa neighboring frequency or amplitude of interstimulus region: forexample, if F₀ is 100 Hz, then a signal-to-noise-ratio may beAmplitude_(100 Hz)/Amplitude_(90 Hz) orAmplitude_(100 Hz)/Amplitude_(interstimulus)). A comparison of theresponse F₀ amplitude to the eliciting sound F₀ amplitude (calculated inthe same manner) can then be made.

Another aspect of F₀ is phase consistency. Phase consistency is ameasure of timing variability of individual frequencies in a response.Phase consistency may also be referred to as phase locking orphase-locking factor. Phase consistency may be calculated over afrequency region that is about 1 Hz, or the frequency region may berange greater than 1 Hz. Methods for calculating F₀ ranges that aregreater than one are described above for both complex sounds with astatic F₀ and complex sounds where F₀ varied.

To calculate phase consistency, first a spectrum is calculated over adiscrete time period of the response using a fast Fourier transform, asdescribed above. This results in a vector that contains a length,indicating the encoding strength of each frequency, and a phase, whichcontains information about the timing of the response to that frequency.To examine the timing variability of the response, each vector istransformed into a unit vector by dividing the FFT by the absolute valueof the FFT. This transformation sets the length component of the vectorto one, discarding the information about encoding strength butmaintaining the phase information. The resultant vector is generated foreach response trial and then averaged across trials so that the lengthof the resulting vector provides a measure of the inter-trial phaseconsistency. It is acceptable to not use every trial. For example,artifact rejecting, or using other criteria, can result in phaseconsistency being calculated on a subset of the sweeps. Alternatively,or in addition, some number of trials may be averaged prior tocalculating phase consistency (e.g., averaging together every 10trials), and/or the trials may be first filtered (provided the filtersdo not exclude the frequency bands of interest). Suitable filters andbandwidths are discussed in section (v). Phase consistency can also becalculated using a bootstrapping method, in which a subset of the trialsare selected at random, phase consistency is calculated across thatsubset of trials, those trials are replaced, and the process is repeatedfor a given number of times.

Instead of or in addition to determining the phase of the signal at agiven time-frequency point, as described above, this approach could beused to extract the frequency of a signal at said point or points. Also,in addition to looking at phase consistency over a single time period inthe response, a sliding window analysis can be used to calculate phaseconsistency over small, overlapping time periods of the response (e.g.,a 40 msec window with a 39 msec overlap would result in phaseconsistency being calculated from 0-40 msec, 1-41 msec, 2-42 msec,etc.).

Other signal processing approaches to determine the instantaneous phaseof the signal at specific frequencies are also known in the artincluding, but not limited to wavelets. Wavelets are convolved with thebrain response signal to provide amplitude and phase information foreach time-frequency point(s), and then procedures follow as above. Thesecould include Morlet wavelets, Mexican hat wavelets, Meyer wavelets, andmore.

Another aspect of a response F₀ is the F₀ frequency error. The “F₀frequency error” is defined as the difference in frequency (Hz) betweenthe F₀ of the complex sound and the maximum spectral peak in the regionof interest in the response. For example, if the largest peak of theresponse from 75-175 Hz was at 125 Hz, and the stimulus F₀ was 100 Hz,then the “F₀ frequency error” would be +25 Hz.

Another aspect of a response F₀ is F₀ sharpness. F₀ sharpness may alsobe referred to as F₀ bandwidth. To determine F₀ sharpness, the F₀ peakin the brain response spectrum is identified as detailed above. Thewidth of the corresponding peak is then selected determining thedifference between the surrounding ends of that peak a pre-specifiedamplitude below that peak, such as 3 dB below the peak, 10 dB below thepeak, or the entire length below the peak. The frequency differencebetween these two boundaries are determined and the ratio between thefrequency difference and the pre-specified amplitude is determined,called the Q. For example, the Q of a peak at 100 Hz, with the bandwidth10 dB below it, would be 10 (100/10). Bandwidth may be determined forpeaks other than F₀, as well.

Another aspect of a response F₀ is pitch tracking. Pitch tracking refersto the extent to which a brain response tracks an F₀ that changes overtime (e.g. a complex sound may have a linear increase in F₀ from 100 to150 Hz over the duration of the sound). The idea is that at any givenpoint in the stimulus, the F₀ is at a given instantaneous frequency. Asan example, perhaps at time 20 msec the instantaneous frequency is 100Hz; at 70 msec it is 125 Hz; at 120 msec, it is at 150 Hz. To determinethese instantaneous frequencies (either in the stimulus or the response)an autocorrelation approach would be applied to small, overlappingsegments of the waveform. For example, to determine the instantaneousfrequency at 20 msec, one might extract a segment of the waveform from 0to 40 msec and apply the autocorrelation technique described above. Theresultant derived fundamental frequency (1/L_(max)) would be assigned totime 20 msec. Then, one would repeat with a segment of the waveform from1 to 41 msec. The resultant derived fundamental frequency (1/L_(max))would be assigned to time 21 msec, etc. In this way, a pitch trackinganalysis can be achieved, utilizing the “frequency error” methoddescribed above. The difference in frequency (Hz) between the F₀ of thestimulus and F₀ of the response could be computed for each time point,and the absolute values of the resulting frequency errors could besummed to compute an overall frequency error score, where 0 indicatesperfect pitch tracking and larger numbers indicate poorer pitchtracking.

ii. Harmonics

Another aspect of a brain response to a complex sound is the extent towhich a brain response reflects the harmonics of the stimulus. Variousaspects may be analyzed including, but not limited to, harmonicamplitude, phase consistency, spectral flux, and spectral centroid.

Suitable methods for analyzing various aspects of the response harmonicsare well-known in the art. These methods include those described for F₀,changing parameters as needed to reflect the frequency range of theharmonics. For example, when determining phase consistency of theharmonics, frequency information outside of the F₀ is analyzed. Thisregion may be as small as 1 Hz, or it may encompass a range ofharmonics. Amplitudes at individual harmonics may also be averagedtogether. In another example, when creating an average of the responsein embodiments where the acoustic stimulus was presented to a subject inmultiple polarities (e.g., condensation and rarefaction) then, theresponses to one of the polarities can be inverted before averaging(i.e., “subtracted’) in order to enhance the response to the harmonics.Alternatively, or in addition, harmonic amplitude may be referenced tothe amplitude of a non-stimulus-evoked portion of the response. Anexample of a non-stimulus-evoked portion of the response would be theinterstimulus period, in other words the response to the silence betweensuccessive stimulus presentations. This interstimulus-period responsewould be considered background activity of the brain, and so computingthe ratio, for example, RMS_(harmonic)/RMS_(interstimulus) would beconsidered a signal to noise ratio (SNR).

iii. Neural Timing

Another aspect of a brain response to a complex sound is the speed ortiming of one or more response peaks of the brain response. The identityand number of response peaks analyzed can vary depending on the acousticstimulus. For example, while all complex sounds elicit an onset peak,not all features are shared by every complex sound.

In some embodiments, one or more transient feature is analyzed. In otherembodiments one or more sustained feature is evaluated. In otherembodiments one or more transient feature and/or one or more sustainedfeature is evaluated. In each of the above embodiments, as few as oneresponse peak may be analyzed or more than one response peak may beanalyzed. When analyzing more than one response peak, the response peaksmay or may not be clustered in the same time region.

As a non-limiting example, if the complex sound was /ada/, a subset ofpeaks in the response time region corresponding to just the /d/ may beanalyzed (accounting for the neural onset delay). Alternatively, or inaddition, the onset peak could be analyzed and/or the consonant-to-voweltransition (or just a portion thereof) could be analyzed. As anotherexample, when a complex sound has a longer duration and encompassesmultiple, discrete features (e.g., complex speech sounds comprisingmultiple phonemes or syllables or a complex sound that is musical melodyor comprised of several musical notes), it might be logical, in thesecases, to perform an analysis over discrete acoustic/phonetic portionsof the complex sound and response.

Methods for identifying response peaks are well-known in the art,aspects of which are briefly described below. See, for example, Skoe etal., Ear & Hearing, 2010, 31(3) and the references disclosed therein.

In one approach, the locations of the stereotyped peaks in a brainresponse may be determined by the individual who collected the data. Themethod typically involves the use of two or more subaverages generatedby a computer to identify where the peaks in a subject's brain responsereliably occur. The peaks are then marked on the final averagedwaveform. Alternatively, a normative database may be used in addition toor instead of subaverages. For example, a table listing expected peaksand typical latency ranges for each peak could be consulted. Inadditional examples, a “norm” response that is the average of all of theindividuals in a normative database could be used, or a subject'sprevious response that already has marked peaks could be used. In yetanother example, an algorithm may be used to identify local minima andmaxima within a predetermined window. For example, a computer coulddetect the timing of the largest amplitude point within a pre-specifiedwindow (e.g., about 6 to 10 msec for an onset peak). A computer programcould use other signal processing approaches to identify these peaks,such as a principal components analysis to identify a peak-to-troughcomplex in the response. Using the /da/ syllable for illustration, acomputer program could identify V and A based on their shape andstatistical modeling of the response vs. a norm. Alternatively still, ahybrid method of the above approaches may be used. For example, analgorithm may initially identify peaks and an individual adjusts them,or vice-versa.

An alternative approach to determine neural timing may use astimulus-response cross-correlation approach, for example as describedbelow. Instead of a correlation value, the timing shift that achievesthe maximum correlation is used to quantify neural timing(L_(max)=neural timing).

A third approach to determine neural timing may involve calculating thephase delay, also known as the group delay of the response. The groupdelay calculates the timing of constituent sinusoids in a complexsignal, and so provides a frequency-specific measure of timing. It isthe rate of change of transmission phase angles of the frequencies inthe signal. It is calculated as the negative first derivative of thephase response of a signal:

${\tau_{}(w)} = {- \frac{d\; {\varphi (w)}}{dw}}$

where T_(g)(w) is the group delay, φ is the phase difference between thesignal and response, and w is the frequency. This can be computed acrossall frequencies (T_(g)(w))) or for individual frequencies in theresponse (T_(g)(φ)). These frequency ranges of interest could bedetermined based on the criteria discussed under F₀ or harmonics.

iv. Response Amplitude

Another aspect of a brain response to a complex sound is the amplitudeof one or more response peaks of the brain response. This aspect isconceptually similar to F₀ amplitude, however, F₀ is a frequency domainmeasurement and response peaks are time domain measurements. In someembodiments, one or more transient feature is analyzed. In otherembodiments one or more sustained feature is evaluated. In otherembodiments one or more transient feature and/or one or more sustainedfeature is evaluated. In each of the above embodiments, as few as oneresponse peak may be analyzed or more than one response peak may beanalyzed. When analyzing more than one response peak, the response peaksmay or may not be clustered in the same time region.

As a non-limiting example, if the complex sound was /ada/, a subset ofpeaks in the response time region corresponding to just the /d/ may beanalyzed (accounting for the neural onset delay). Alternatively, or inaddition, the onset peak could be analyzed and/or the consonant-to-voweltransition (or just a portion thereof) could be analyzed. As anotherexample, when a complex sound has a longer duration and encompassesmultiple, discrete features (e.g., complex speech sounds comprisingmultiple phonemes or syllables or a complex sound that is musical melodyor comprised of several musical notes), it might be logical, in thesecases, to perform an analysis over discrete acoustic/phonetic portionsof the complex sound and response.

Methods for identifying response peaks, and regions of peaks, arediscussed above. Computational methods suitable for determining aresponse amplitude for an individual peak or a region comprisingmultiple peaks are known in the art and include, but are not limited to,arithmetic mean amplitude over a region, the root-mean-squared [RMS]amplitude of the peak or region, mean amplitude of the points, max pointminus min point (i.e., peak-to-peak maximum), sum of the points in therectified waveform, amplitude at a single frequency point, the totalamplitude over a region of interest (summed, integer, root-mean-squaredamplitude), etc.

In certain embodiments, the amplitude of a response peak may bereferenced to the amplitude of a non-stimulus-evoked portion of theresponse. An example of a non-stimulus-evoked portion of the responsewould be the interstimulus period, in other words the response to thesilence between successive stimulus presentations. Thisinterstimulus-period response would be considered background activity ofthe brain, and so computing the ratio RMS_(response)/RMS_(interstimulus)would be considered a signal to noise ratio (SNR). If desired, an SNRmay be expressed in decibels (dB) by taking the 10-base log of the RMSamplitude ratio and multiplying by 20.

A comparison of a response peak amplitude to the eliciting soundresponse peak amplitude (calculated in the same manner) can then bemade.

v. Stimulus-Response Correlation

Another aspect of a brain response to a complex sound is the extent towhich the response resembles the evoking sound. Stimulus-responsecorrelations may be performed in the time domain or the frequencydomain.

To determine stimulus-response correlation in the time, an acousticstimulus may be filtered across a bandwidth to match the response andeach subject's response may be cross-correlated to the filteredstimulus. Other suitable methods known in the art may also be used.

The type of filter may vary(e.g., Butterworth, Chebyshev, elliptic,Kaiser, etc.), as may the order (e.g., first-order, second-order, etc.)which is also known as the number of poles. The higher the order, theless energy is present outside the specified filter bandwidth.

The bandwidth across which the filter is applied may vary. Generallyspeaking, an acoustic stimulus will have higher frequency content than afrequency following response (FFR) from the brain. Therefore, low-passfiltering of the acoustic stimulus will result in a stimulus waveformthat correlates better with the FFR. To select the low-pass filtercutoff, one approach is to match the bandwidth to that of the FFRrecording's bandwidth. A second approach is to choose a low-pass filterthat approaches the actual frequency content of the FFR. This approachmight result in a low-pass filter of about 1000 Hz because typically anenvelope-dominated FFR will have little energy above 1000 Hz. Likewise,the choice of high-pass filter may be matched to the FFR recording ormay some other value that approximates the lowest frequency present inthe FFR collection.

The time window selected for performing the cross-correlation may vary.In one approach, when the complex sound is a speech sound, a selectedtime window may correspond roughly to the fully-voiced portion of thestimulus. For example, the time window described in the examples for the/d/ stimulus omits the unvoiced consonant release and the transient FFRcomponent corresponding to the onset of voicing. Other time windows,encompassing a voiced (i.e. periodic) response waveform might also beselected. For example, longer speech stimuli may encompass multiplephonemes or syllables. It might be logical, in these cases, to performthis analysis over discrete acoustic/phonetic portions of the stimulusand response. For example, just the voiced portion of a consonanttransition. Or, just a steady-state vowel portion. Similar conceptsapply to other complex sounds.

The cross-correlation function is a standard procedure that time-shiftsone waveform (A) with respect to another (B) and correlates A to B atmany such time shifts. For example A(1:10) (i.e., points 1 to 10 ofwaveform A) is correlated to B(1:10), then A(1:10) is correlated toB(2:11), then B(3:12), etc. The reverse shift also is evaluated, such atA(1:10) is correlated with B(−1:9) and B(−2:8) etc. Each time shift isconsidered a “lag,” such that A(1:10) vs B(1:10) has a lag of 0. A(1:10)vs B(2:11) has a lag of 1, etc. Pearson product-moment correlation,point-biserial, or Spearman techniques may be used to create acorrelation score. For example, the Pearson product-moment correlationproduces an “r” score. This results in a value scaled from −1 to +1,with +1 meaning perfect correlation, −1 meaning perfect anti-correlation(i.e., identical, but fully out-of-phase), and 0 meaning complete lackof correlation. A type of correlation that produces values outside the−1 to +1 range might also be used.

In performing the cross-correlation, the time-shift (lag) which producesthe maximum Pearson's r value (or value produced by another method) issought. However, there are logical constraints to the lag. For example,it is illogical that the brain response would occur before the stimulus.Therefore, negative lag values are not considered. Likewise, it is knownthat it takes about 6-10 msec for the auditory pathway to respond to asound and to propagate the signal to the recording electrodes. Thereforea lag smaller than about 6 msec would likewise be illogical because itis simply not biologically possible. It is also known that it typicallydoes not take longer than about 10-12 msec for a signal to arise. So, an“appropriate lag” is typically a range of about 6 to about 15 msec, orabout 6 to about 12 msec. A slightly different lag would also beacceptable.

When performing parametric statistical analysis on Pearson's correlationdata, it is a routine procedure to calculate a Fisher-transformed zscore. While not strictly necessary, statistical conclusions drawn fromnon-transformed data may be suspect. This is a mathematical,natural-log-based, transformation that normalizes the r distribution sothat all delta-r values, along the −1 to +1 range are equivalentlyconstant. That is, the variance of untransformed r-values that aretoward the ends of the range (near −1 or near +1) is much smaller thanthe variance of r-values at the middle of the range (near 0).

All descriptions and alternatives described above involve time-domaincomparisons between an acoustic stimulus and its evoked response.Correlations could also be performed between frequency-domain waveformsof the stimulus and response. The major difference, aside from thefrequency-domain conversion itself, is that the allowance for the lagwould have to be made in the time domain prior to frequency-domainconversion and a straight (i.e., non-cross-) correlation would beperformed. For example, let's say one was interested in afrequency-domain correlation of neural activity to the 20 to 80 msecportion of a particular stimulus. If a typical response, due neuralpropagation time, arises 8 msec after the stimulus, one would perform afrequency-domain conversion of the 20-80 msec segment of the stimulusand of the 28-88 msec segment of the response. Then, once in thefrequency domain, a straight correlation (lag=0) would be performed.

vi. Response Consistency

Another aspect of a brain response to a complex sound is the extent towhich every presentation of the same acoustic stimulus (each a “trial”)results in the same brain response. This may also be referred to as thestability of response. Response-consistency calculations may beperformed in the time domain or the frequency domain. In addition,response-consistency calculations may be performed on an added waveform(e.g., opposing-polarity stimulus presentations are added) or asubtracted waveform (e.g., opposing-polarity stimulus presentationsresults subtracted/the responses to one of the polarities can beinverted).

In one approach, approximately half of the trials are randomly selectedand averaged, and the remaining trials are averaged. The twosub-averaged waveforms are then correlated over a time window todetermine their similarity. The time window can vary, as described abovefor stimulus-response correlation. Suitable methods for calculating acorrelation score are known in the art and include, but are not limitedto Pearson product-moment correlation, point-biserial, or Spearmantechniques; correlation data may be Fisher-transformed to a z scorebefore averaging. These steps are then repeated a number of differenttimes, each repetition with a different random samplings of trials, andthe correlation values from each repetition are averaged (arithmeticmean) to generate a final measure of inter-trial response consistency.The number of repetitions can vary, but should be selected to provideconfidence that the final mean correlation value is a goodrepresentation of the underlying data. Another approach is not tomaintain individual trials, but rather collect two discrete subaverages.

In certain embodiments, the amplitude of a response peak may bereferenced to the amplitude of a non-stimulus-evoked portion of theresponse. An example of a non-stimulus-evoked portion of the responsewould be the interstimulus period, in other words the response to thesilence between successive stimulus presentations. Thisinterstimulus-period response would be considered background activity ofthe brain, and so computing the ratio response/interstimulus would beconsidered a signal to noise ratio (SNR).

vii. Difference Measures

A difference measure is a means of quantifying a change in a measure.For example, a difference measure may provide a means of quantifying achange in a response component in the same subject after time haspassed, or after injury or intervention has taken place. A differencemeasure is also a means to quantify a difference in the same responsecomponent(s) to two (or more) different stimuli in the same subject.Additionally or alternatively, a difference measure may be applied totwo measures within a single response. For example, the timingdifference between peaks V and A, the phase-locking ratio between the F₀and one or more harmonics, the amplitude ratio between multipleharmonics, the RMS amplitude difference between added- andsubtracted-polarity responses, etc.

Difference measures may be expressed as a percent change (e.g., increaseor decrease), as absolute terms (e.g., delay in msec; decrease inmagnitude in μV, increase in frequency error in Hz, decrease in responseconsistency in r, etc.),or as a dB difference.

In embodiments where an acoustic stimulus comprises background noise, adifference measure may be a change in a response component in thepresence of background noise as compared to the absence of backgroundnoise. For example, background noise is known to diminish responseamplitudes, so one may wish to determine the percent reduction of F₀amplitude when background noise is added to the acoustic stimulus. Anyof the above listed measurements can be evaluated.

Examples of other contexts in which two or more responses could becompared include: changes in one or more frequencies in the sound (a /d/with a high pitch vs a /d/ with a low pitch); different speech sounds (a/d/ compared to a /b/); sounds of varying amplitude modulation index,also known as modulation depth (the extent to which, in a complexsignal, the ratio of the excursions of the modulated signal to theunmodulated signal, resulting in the degree of local amplitude envelopevariation between two consecutive peaks in the signal); musical soundsof different pitch or timbre; etc.

II. Methods

(a) Identifying Non-Penetrating Brain Injury and/or Classifying aSubject

The present disclosure provides methods for identifying non-penetratingbrain injury in a subject, as well as methods for classifying a subjectthat received a hit to the body that transmitted an impulsive force tothe brain as either having a non-penetrating brain injury or not.

In one aspect, the method comprises analyzing one or more components ofthe subject's brain response to an acoustic stimulus comprising acomplex sound; and identifying the subject as having a non-penetratingbrain injury when a value for at least one component of the brainresponse is anomalous, wherein the component(s) is selected from thegroup consisting of fundamental frequency (F₀) and/or harmonics, neuraltiming of a response peak, response amplitude over a time window thatencompasses some or all of a sustained response, and stimulus-responsecorrelation over a time window that encompasses some or all of asustained response. In some embodiments, the method further comprisesanalyzing one or more transient responses to an acoustic stimulus. Thecomplex sound is selected from those described in Section I(a). In someembodiments, a complex sound comprises a musical sound. In otherembodiments, a complex sound comprises an environmental sound. In someembodiments, a complex sound comprises a speech sound or a non-speechvocal sound. In some embodiments, a complex sound comprises a firstsound that transitions directly to a second sound, wherein the firstsound has an attack substantially similar to an obstruent consonant andthe second sound has a minimum of two formants held steady for oneperiod of F₀. Methods for recording a brain response to an acousticstimulus are known in the art, and further detailed in SectionI(a)(iii).

In another aspect, the method comprises analyzing one or more componentsof the subject's brain response to an acoustic stimulus comprising acomplex sound; and identifying the subject as having a non-penetratingbrain injury when a value for at least one component of the brainresponse is anomalous, wherein the component(s) is selected from thegroup consisting of fundamental frequency (F₀) and/or harmonics, neuraltiming of a sustained response peak, response amplitude over some or allof a consonant-vowel transition, and stimulus-response correlation overa time window that encompasses some or all of a sustained response. Insome embodiments, the method further comprises analyzing one or moretransient responses to an acoustic stimulus. The complex sound isselected from those described in Section I(a). In some embodiments, acomplex sound comprises a speech sound or a non-speech vocal sound. Inother embodiments, a complex sound comprises a first sound thattransitions directly to a second sound, wherein the first sound is anobstruent consonant and the second sound has a minimum of two formantsheld steady for one period of F₀. In other embodiments, a complex soundcomprises a consonant, a consonant-to-vowel transition, and optionally avowel. In other embodiments, a complex sound comprises a consonant, aconsonant-to-vowel transition, and optionally a vowel, wherein theconsonant is an obstruent stop consonant. In other embodiments, acomplex sound comprises a consonant, a consonant-to-vowel transition,and optionally a vowel, wherein the consonant is an obstruent stopconsonant and the vowel is a low, back vowel. In other embodiments, acomplex sound comprises a speech syllable selected from the groupconsisting of /da/, /pa/, /ka/, /ta/, /ba/, and /ga/. Methods forrecording a brain response to an acoustic stimulus are known in the art,and further detailed in Section I(a)(iii).

In another aspect, the method comprises (a) fitting the subject withelectrodes to measure voltage potentials generated from the subject'sbrain; (b) administering to the subject an acoustic stimulus, whereinthe acoustic stimulus comprises a complex sound; (c) recording voltagepotentials from the subject's brain for at least the duration of theacoustic stimulus; (d) analyzing the voltage potentials to determine oneor more components of the brain response; and (e) identifying thesubject as having a non-penetrating brain injury when a value for atleast one component of the brain response is anomalous, wherein thecomponent(s) is selected from the group consisting of fundamentalfrequency (F₀) and/or harmonics, neural timing of a response peak,response amplitude over a time window that encompasses some or all of asustained response, and stimulus-response correlation over a time windowthat encompasses some or all of a sustained response. In someembodiments, the method further comprises analyzing one or moretransient responses to an acoustic stimulus. The complex sound isselected from those described in Section I(a). In some embodiments, acomplex sound comprises a musical sound. In other embodiments, a complexsound comprises an environmental sound. In some embodiments, a complexsound comprises a speech sound or a non-speech vocal sound. In someembodiments, comprises a first sound that transitions directly to asecond sound, wherein the first sound has an attack substantiallysimilar to an obstruent consonant and the second sound has a minimum oftwo formants held steady for one period of F₀. Methods for recording abrain response to an acoustic stimulus are known in the art, and furtherdetailed in Section I(a)(iii). [

In another aspect, the method comprises (a) fitting the subject withelectrodes to measure voltage potentials generated from the subject'sbrain; (b) administering to the subject an acoustic stimulus, whereinthe acoustic stimulus comprises a complex sound; (c) recording voltagepotentials from the subject's brain for at least the duration of theacoustic stimulus; (d) analyzing the voltage potentials to determine oneor more components of the brain response; and (e) identifying thesubject as having a non-penetrating brain injury when a value for atleast one component of the brain response is anomalous, wherein thecomponent(s) is selected from the group consisting of fundamentalfrequency (F₀) and/or harmonics, neural timing of a sustained responsepeak, response amplitude over some or all of a consonant-voweltransition, and stimulus-response correlation over a time window thatencompasses a sustained response. In some embodiments, the methodfurther comprises analyzing one or more transient responses to anacoustic stimulus. The complex sound is selected from those described inSection I(a). In some embodiments, a complex sound comprises a speechsound or a non-speech vocal sound. In other embodiments, a complex soundcomprises a first sound that transitions directly to a second sound,wherein the first sound is an obstruent consonant and the second soundhas a minimum of two formants held steady for one period of F₀. In otherembodiments, a complex sound comprises a first sound that transitionsdirectly to a second sound, wherein the first sound is an obstruentconsonant and the second sound has a minimum of two formants held steadyfor one period of F₀. In other embodiments, a complex sound comprises aconsonant, a consonant-to-vowel transition, and optionally a vowel. Inother embodiments, a complex sound comprises a consonant, aconsonant-to-vowel transition, and optionally a vowel, wherein theconsonant is an obstruent stop consonant. In other embodiments, acomplex sound comprises a consonant, a consonant-to-vowel transition,and optionally a vowel, wherein the consonant is an obstruent stopconsonant and the vowel is a low, back vowel. In other embodiments, acomplex sound comprises a speech syllable selected from the groupconsisting of /da/, /pa/, /ka/, /ta/, /ba/, and /ga/. Methods forrecording a brain response to an acoustic stimulus are known in the art,and further detailed in Section I(a)(iii).

In another aspect, the method comprises analyzing one or more componentsof the subject's brain response to an acoustic stimulus comprising acomplex sound; and classifying the subject as having a non-penetratingbrain injury when a value for at least one component of the brainresponse is anomalous, wherein the component(s) is selected from thegroup consisting of fundamental frequency (F₀) and/or harmonics, neuraltiming of a response peak, response amplitude over a time window thatencompasses some or all of a sustained response, and stimulus-responsecorrelation over a time window that encompasses some or all of asustained response. In some embodiments, the method further comprisesanalyzing one or more transient responses to an acoustic stimulus. Thecomplex sound is selected from those described in Section I(a). In someembodiments, a complex sound comprises a musical sound. In otherembodiments, a complex sound comprises an environmental sound. In someembodiments, a complex sound comprises a speech sound or a non-speechvocal sound. In some embodiments, a complex sound comprises a firstsound that transitions directly to a second sound, wherein the firstsound has an attack substantially similar to an obstruent consonant andthe second sound has a minimum of two formants held steady for oneperiod of F₀. Methods for recording a brain response to an acousticstimulus are known in the art, and further detailed in SectionI(a)(iii).

In another aspect, the method comprises analyzing one or more componentsof the subject's brain response to an acoustic stimulus comprising acomplex sound; and classifying the subject as having a non-penetratingbrain injury when a value for at least one component of the brainresponse is anomalous, wherein the component(s) is selected from thegroup consisting of fundamental frequency (F₀) and/or harmonics, neuraltiming of a sustained response peak, response amplitude over some or allof a consonant-vowel transition, and stimulus-response correlation overa time window that encompasses some or all of a sustained response. Insome embodiments, the method further comprises analyzing one or moretransient responses to an acoustic stimulus. The complex sound isselected from those described in Section I(a). In some embodiments, acomplex sound comprises a speech sound or a non-speech vocal sound. Inother embodiments, a complex sound comprises a first sound thattransitions directly to a second sound, wherein the first sound is anobstruent consonant and the second sound has a minimum of two formantsheld steady for one period of F₀. In other embodiments, a complex soundcomprises a consonant, a consonant-to-vowel transition, and optionally avowel. In other embodiments, a complex sound comprises a consonant, aconsonant-to-vowel transition, and optionally a vowel, wherein theconsonant is an obstruent stop consonant. In other embodiments, acomplex sound comprises a consonant, a consonant-to-vowel transition,and optionally a vowel, wherein the consonant is an obstruent stopconsonant and the vowel is a low, back vowel. In other embodiments, acomplex sound comprises a speech syllable selected from the groupconsisting of /da/, /pa/, /ka/, /ta/, /ba/, and /ga/. Methods forrecording a brain response to an acoustic stimulus are known in the art,and further detailed in Section I(a)(iii).

In another aspect, the method comprises (a) fitting the subject withelectrodes to measure voltage potentials generated from the subject'sbrain; (b) administering to the subject an acoustic stimulus, whereinthe acoustic stimulus comprises a complex sound; (c) recording voltagepotentials from the subject's brain for at least the duration of theacoustic stimulus; (d) analyzing the voltage potentials to determine oneor more components of the brain response; and (e) classifying thesubject as having a non-penetrating brain injury when a value for atleast one component of the brain response is anomalous, wherein thecomponent(s) is selected from the group consisting of fundamentalfrequency (F₀) and/or harmonics, neural timing of a response peak,response amplitude over a time window that encompasses some or all of asustained response, and stimulus-response correlation over a time windowthat encompasses some or all of a sustained response. In someembodiments, the method further comprises analyzing one or moretransient responses to an acoustic stimulus. The complex sound isselected from those described in Section I(a). In some embodiments, acomplex sound comprises a musical sound. In other embodiments, a complexsound comprises an environmental sound. In some embodiments, a complexsound comprises a speech sound or a non-speech vocal sound. In someembodiments, comprises a first sound that transitions directly to asecond sound, wherein the first sound has an attack substantiallysimilar to an obstruent consonant and the second sound has a minimum oftwo formants held steady for one period of F₀. Methods for recording abrain response to an acoustic stimulus are known in the art, and furtherdetailed in Section I(a)(iii).

In another aspect, the method comprises (a) fitting the subject withelectrodes to measure voltage potentials generated from the subject'sbrain; (b) administering to the subject an acoustic stimulus, whereinthe acoustic stimulus comprises a complex sound; (c) recording voltagepotentials from the subject's brain for at least the duration of theacoustic stimulus; (d) analyzing the voltage potentials to determine oneor more components of the brain response; and (e) classifying thesubject as having a non-penetrating brain injury when a value for atleast one component of the brain response is anomalous, wherein thecomponent(s) is selected from the group consisting of fundamentalfrequency (F₀) and/or harmonics, neural timing of a sustained responsepeak, response amplitude over some or all of a consonant-voweltransition, and stimulus-response correlation over a time window thatencompasses some or all of a sustained response. In some embodiments,the method further comprises analyzing one or more transient responsesto an acoustic stimulus. The complex sound is selected from thosedescribed in Section I(a). In some embodiments, a complex soundcomprises a speech sound or a non-speech vocal sound. In otherembodiments, a complex sound comprises a first sound that transitionsdirectly to a second sound, wherein the first sound is an obstruentconsonant and the second sound has a minimum of two formants held steadyfor one period of F₀. In other embodiments, a complex sound comprises aconsonant, a consonant-to-vowel transition, and optionally a vowel. Inother embodiments, a complex sound comprises a consonant, aconsonant-to-vowel transition, and optionally a vowel, wherein theconsonant is an obstruent stop consonant. In other embodiments, acomplex sound comprises a consonant, a consonant-to-vowel transition,and optionally a vowel, wherein the consonant is an obstruent stopconsonant and the vowel is a low, back vowel. In other embodiments, acomplex sound comprises a speech syllable selected from the groupconsisting of /da/, /pa/, /ka/, /ta/, /ba/, and /ga/. Methods forrecording a brain response to an acoustic stimulus are known in the art,and further detailed in Section I(a)(iii).

In each of the above aspects, the subject may be symptomatic orasymptomatic.

In each of the above aspects, the term “anomalous value” refers to adeviation from the value for a control group or a normative value or adeviation from a previously established value for the subject (i.e., a“baseline value”), wherein the deviation exceeds the difference expectedby chance. When an anomalous value is deviation from a value for acontrol group, the members of the control group may have never beendiagnosed with a non-penetrating brain injury. Alternatively, themembers of the control group may be a group of subjects that have neverbeen diagnosed with a concussion. In another example, the control groupmay be a demographic subsample based on relevant information about thesubject including, but not limited to, the subjects age and/or lifeexperiences (e.g., number of years playing a contact sport, number ofyears in the military, number of years in a combat/war zone, number ofcar accidents, number of concussions, etc.). When an anomalous value isa deviation from a previously established value for the subject (i.e., a“baseline value”), the value may have been established before a subjectwas diagnosed with a non-penetrating brain injury including, but notlimited to, a concussion or traumatic brain injury (TBI). Alternatively,a baseline value may have been established at a significant point intime—e.g., the start of a sports season, the start of a game or acompetition, enlistment into the military, deployment to a combat/warzone, the start of employment, etc. A baseline value may also be thefirst available measurement for a subject. When an anomalous value isdeviation from a value a normative value, the normative value may beobtained from published sources.

Suitable methods for determining whether a deviation exceeds thedifference expected by chance are well-known in the art. For example, ananalysis of statistical deviation may be based on probabilitydistributions based on raw values or normalized values (e.g., z-scores,etc.), wherein one-half standard deviation or more (e.g. 1, 2, 3, 4, 5,or more) indicates a deviation that exceeds the difference expected bychance. Alternatively, a score or value may be converted to percentilesbased on established value (e.g., an entire population's performance orbased on a demographic subsample), wherein performance at or below the50^(th) percentile, the 45^(th) percentile, the 40^(th) percentile, the35^(th) percentile, the 35^(th) percentile, the 30^(th) percentile, the25^(th) percentile, the 20^(th) percentile, the 15^(th) percentile, the10^(th) percentile, or the 5^(th) percentile indicates a deviation thatexceeds the difference expected by chance. In another example, adeviation that exceeds the difference expected by chance may be adifference determined by the minimum detectable change (MDC). The MDC isa statistic reflecting the smallest amount of deviation in a patient'sscore that ensures the change is not the result of measurement error,defined as 1.96 x the standard error of the mean on the test×√2. Inanother example, a deviation that exceeds the difference expected bychance may be determined by confidence intervals—a range of values inwhich there is a specified probability that the value of a measure lieswithin said range in a healthy population. For example, if a healthypopulation has a 90% confidence interval of 100-120, then in 90% ofhypothetical cases we would predict performance in the population tofall within 100-120, so a score outside the confidence interval would beunlikely and anomalous. Common confidence interval ranges include 90%,95%, and 99%. In another example, a deviation that exceeds thedifference expected by chance may be determined by a statistical test todetermine if a score falls outside of the range of expected based onfrequentist probability theory, such as a student's t-test, an analysisof variance, a regression, a Mann-Whitney U test, a chi-square test,etc. In another example, a deviation that exceeds the differenceexpected by chance may be determined by a statistical test to determineif a score falls outside of the range of expected values based onBayesian statistical theory. In another example, a deviation thatexceeds the difference expected by chance may be a score that exceeds athreshold determined by research. This may be a categorical threshold(such as a body temperature over 100° qualifies as a fever) or it may bea threshold from a statistical algorithm that balances the probabilityof receiving true positive and true negative results. In embodimentswhere the deviation is value that exceeds a threshold from a statisticalalgorithm, the threshold typically produces at least an 80% sensitivity(true positive rate) and an 80% specificity (true negative rate), andhas a strong predictive utility (e.g. as indicated by a ReceiverOperating Characteristic (ROC)≧0.80, preferably, ≧0.85, morepreferably≧0.90; other types of predictive values include PPV, NPV,etc.). In another example, when the anomalous value refers to adeviation from a previously established value for the subject, adeviation that exceeds the difference expected by chance may be adifference in score that exceeds the threshold expected based on thetest-retest performance in a healthy population. For example, if testsin a healthy population showed that an F₀ amplitude is expected to varyby 3 μV between tests, then a difference of about 4 μV between tests ina patient would be considered anomalous.

In each of the above aspects, “a value for at least one component of thebrain response” includes one, two, three, four, five or more values forone or more components independently selected from the recited groups,wherein at least one component is an aspect of the frequency followingresponse. Components that are an aspect of the frequency followingresponse include fundamental frequency (F₀), harmonics, neural timing ofa sustained response peak, response amplitude over a time window thatencompasses some or all of a sustained response, and stimulus-responsecorrelation a time window that encompasses some or all of a sustainedresponse. As stated above, the method may further comprise analyzing oneor more transient responses. In some embodiments, a transient responsemay be the timing or amplitude of an onset peak or onset response. Inembodiments where the complex sound comprises one or more amplitudebursts, a suitable transient response may be the timing or amplitude ofa transient response to the onset of one or more of the amplitudebursts.

In each of the above aspects, “a value for at least one component of thebrain response” also includes embodiments where brain response valuesare combined together using a statistical model to produce a newmeasurement and the new measurement is anomalous, as defined above. Astatistical model may have one or multiple steps. In embodiments where astatistical model has one step, two or more values are used in thesingle step. In embodiments where a statistical model has two or moresteps, each step may consist of a single value or combine one or morevalues. For example, a first step may control for demographic factorsthat could have an effect on the FFR independent of brain injury.Non-limiting examples of values that may be included in the first stepinclude age, gender, pre-existing medical conditions, background noisein the FFR (e.g., amplitude of non-stimulus related neural activity,such as in the interstimulus region, etc.), timing of the onset peak(e.g. wave V in response to a click), etc. One or more additional stepsmay then incorporate one or more values for a component that is anaspect of the frequency following response (e.g., two, three, four, fiveor more values for one or more components independently selected fromthe recited groups). Again, value(s) for one or more transient responsesmay be included with the FFR values. Suitable models should have a NPVand a PPV greater than about 80%, preferably greater than about 85%,more preferably greater than about 90%; and/or an ROC curve with an AUCvalue greater than about 0.80, preferably greater than about 0.85, morepreferably greater than about 0.90.

In an exemplary embodiment, at least one anomalous value comprises F₀and stimulus-response correlation over a time window that encompassessome or all of a sustained response. In another exemplary embodiment, atleast one anomalous value comprises F₀ and amplitude of the onsetresponse. In another exemplary embodiment, at least one anomalous valuecomprises stimulus-response correlation over a time window thatencompasses some or all of a sustained response and amplitude of theonset response.

In embodiments where a value of fundamental frequency (F₀) is anomalous,the anomalous value may be F₀ amplitude, F₀ phase consistency, F₀sharpness, F₀ frequency error, pitch tracking, or any combinationthereof. Methods for determining these values are described in SectionI(b)(i).

In embodiments where a value for neural timing of a response peak isanomalous, one, two, three, or more response peaks may have anomalousvalues. In certain embodiments, a value for neural timing of at leastone sustained response peak is anomalous. As a non-limiting example, ifthe complex sound comprises /da/, the timing of peak A, peak D, or peakE may be anomalous, as well as any combination thereof. Methods fordetermining neural timing of a sustained response peak(s) are describedin Section I(b)(iii).

In embodiments where response amplitude over some or all of the FFR isanomalous, the time window over which the response amplitude iscalculated may be a portion of the FFR or the entire FFR. In preferredembodiments, the time window over which response amplitude is calculatedincludes at least one formant, preferably at least two formants (orequivalent of a formant for non-speech sounds). As a non-limitingexample, when a complex sound comprises a consonant-to-vowel transitionand response amplitude over the consonant-to-vowel transition isanomalous, the time range over which the response amplitude iscalculated may optionally include unvoiced consonant release and/or thetransient FFR component corresponding to the onset of voicing. When acomplex sounds comprises more than one consonant-to-vowel transition,the response amplitude over the consonant-to-vowel transition may or maynot be anomalous at each transition. Methods for determining theresponse amplitude over a FFR region are described in Section I(b)(iv).

In embodiments where a stimulus-response correlation value over a timewindow that encompasses some or all of a sustained response isanomalous, the anomalous value may be a time-domain measurement, afrequency-domain measurement, or both. In preferred embodiments, thetime window includes at least one formant, preferably at least twoformants (or equivalent of a formant for non-speech sounds). As anon-limiting example, when the complex sound is a speech sound, the timewindow may comprise a portion, or all, of a voiced response, includingbut not limited to a consonant-to-vowel transition, a voiced portion ofa consonant transition, or a steady-state vowel portion. Methods fordetermining stimulus-response correlation values are described inSection I(b)(v).

(b) Determining a Change in a Non-Penetrating Brain Injury

The present disclosure also provides methods for determining a change innon-penetrating brain injury. These methods may be used to assess asubject's recovery from a non-penetrating brain injury to determine ifthe brain injury is worsening, improving, or has stayed the same.Subjects recovering from non-penetrating brain injury may or may notreceive a therapeutic intervention. Non-limiting examples of types oftherapeutic interventions include pharmaceutical, psychological (e.g.memory tests or brain “exercises”), auditory, and behavioral. Forexample, a subject recovering from non-penetrating brain injury maysimply have been advised to rest and/or refrain from activities that mayfurther worsen the non-penetrating brain injury. Alternatively, asubject recovering from non-penetrating brain injury may be involvementin a treatment program with the goal of speeding recovery or recoveringaspects of brain function that would not have returned but for thetreatment. A determination that the non-penetrating brain injury isworsening or has stayed the same may result in the start of atherapeutic intervention, a change in the type of therapeuticintervention, and/or or a modification of an existing therapeuticintervention; and/or advisement that the subject should refrain fromactivities that may further worsen the non-penetrating brain injury. Adetermination that the non-penetrating brain injury has improved or hasstayed the same may result in the maintenance, change and/ordiscontinuation a therapeutic intervention, and/or may result in thesubject being cleared to resume all activities.

In one aspect, the method comprises (a) analyzing one or more componentsof a subject's brain response to an acoustic stimulus comprising acomplex sound; (b) re-testing the subject's brain response to theacoustic stimulus at a later time; and determining any differences inthe one or more components from step (a); wherein the component(s) isselected from the group consisting of fundamental frequency (F₀) and/orharmonics, neural timing of a response peak, response amplitude over atime window that encompasses some or all of a sustained response, andstimulus-response correlation over a time window that encompasses someor all of a sustained response. If the absolute value of the differenceis greater than would be expected by chance, there is a change in thenon-penetrating brain injury. In some embodiments, a complex soundcomprises a speech sound or a non-speech vocal sound. In someembodiments, comprises a first sound that transitions directly to asecond sound, wherein the first sound has an attack substantiallysimilar to an obstruent consonant and the second sound has a minimum oftwo formants held steady for one period of F₀. Methods for recording abrain response to an acoustic stimulus are known in the art, and furtherdetailed in Section I(a)(iii).

In another aspect, the method comprises (a) analyzing one or morecomponents of a subject's brain response to an acoustic stimuluscomprising a complex sound; (b) re-testing the subject's brain responseto the acoustic stimulus at a later time; and determining anydifferences in the one or more components from step (a); wherein thecomponent(s) is selected from the group consisting of fundamentalfrequency (F₀), neural timing of a sustained response peak, responseamplitude over a time window that encompasses some or all of aconsonant-to-vowel transition, and stimulus-response correlation over atime window that encompasses some or all of a sustained response. If theabsolute value of the difference is greater than would be expected bychance, there is a change in the non-penetrating brain injury. In someembodiments, a component is an aspect of the frequency followingresponse. The complex sound is selected from those described in SectionI(a). In some embodiments, a complex sound comprises a musical sound. Inother embodiments, a complex sound comprises an environmental sound. Insome embodiments, a complex sound comprises a speech sound or anon-speech vocal sound. In other embodiments, a complex sound comprisesa first sound that transitions directly to a second sound, wherein thefirst sound is an obstruent consonant and the second sound has a minimumof two formants held steady for one period of F₀. In other embodiments,a complex sound comprises a consonant, a consonant-to-vowel transition,and optionally a vowel. In other embodiments, a complex sound comprisesa consonant, a consonant-to-vowel transition, and optionally a vowel,wherein the consonant is an obstruent stop consonant. In otherembodiments, a complex sound comprises a consonant, a consonant-to-voweltransition, and optionally a vowel, wherein the consonant is anobstruent stop consonant and the vowel is a low, back vowel. In otherembodiments, a complex sound comprises a speech syllable selected fromthe group consisting of /da/, /pa/, /ka/, /ta/, /ba/, and /ga/. Methodsfor recording a brain response to an acoustic stimulus are known in theart, and further detailed in Section I(a)(iii).

In another aspect, the method comprises two steps. The first step, i.e.,step (a), tests a subject's brain response to an acoustic stimulus by:(1) fitting the subject with electrodes to measure voltage potentialsgenerated from the subject's brain; (2) administering to the subject anacoustic stimulus, wherein the acoustic stimulus comprises a complexsound; (3) recording voltage potentials from the subject's brain for atleast the duration of the acoustic stimulus; and (4) analyzing thevoltage potentials to determine one or more components of the brainresponse; wherein the component(s) is selected from the group consistingof fundamental frequency (F₀), neural timing of a response peak,response amplitude over a time window that encompasses some or all of asustained response, and stimulus-response correlation over a time windowthat encompasses some or all of a sustained response. The second step,i.e., step (b), re-tests a subject's brain response to the same acousticstimulus by repeating steps (a)(1)-(4) and determining any differencesin the one or more components from step (a). If the absolute value ofthe difference is greater than would be expected by chance, there is achange in the non-penetrating brain injury. The complex sound isselected from those described in Section I(a). In some embodiments, acomplex sound comprises a musical sound. In other embodiments, a complexsound comprises an environmental sound. In some embodiments, a complexsound comprises a speech sound or a non-speech vocal sound. In someembodiments, comprises a first sound that transitions directly to asecond sound, wherein the first sound has an attack substantiallysimilar to an obstruent consonant and the second sound has a minimum oftwo formants held steady for one period of F₀. Methods for recording abrain response to an acoustic stimulus are known in the art, and furtherdetailed in Section I(a)(iii).

In another aspect, the method comprises two steps. The first step, i.e.,step (a), tests a subject's brain response to an acoustic stimulus by:(1) fitting the subject with electrodes to measure voltage potentialsgenerated from the subject's brain; (2) administering to the subject anacoustic stimulus, wherein the acoustic stimulus comprises a complexsound; (3) recording voltage potentials from the subject's brain for atleast the duration of the acoustic stimulus; and (4) analyzing thevoltage potentials to determine one or more components of the brainresponse; wherein the component(s) is selected from the group consistingof fundamental frequency (F₀) and/or harmonics, neural timing of asustained response peak, response amplitude over a time window thatencompasses some or all of a consonant-to-vowel transition, andstimulus-response correlation over a time window that encompasses someor all of a sustained response. The second step, i.e., step (b),re-tests a subject's brain response to the same acoustic stimulus byrepeating steps (a)(1)-(4) and determining any differences in the one ormore components from step (a). If the absolute value of the differenceis greater than would be expected by chance, there is a change in thenon-penetrating brain injury. The complex sound is selected from thosedescribed in Section I(a). In some embodiments, a complex soundcomprises a speech sound or a non-speech vocal sound. In otherembodiments, a complex sound comprises a first sound that transitionsdirectly to a second sound, wherein the first sound is an obstruentconsonant and the second sound has a minimum of two formants held steadyfor one period of F₀. In other embodiments, a complex sound comprises aconsonant, a consonant-to-vowel transition, and optionally a vowel. Inother embodiments, a complex sound comprises a consonant, aconsonant-to-vowel transition, and optionally a vowel, wherein theconsonant is an obstruent stop consonant. In other embodiments, acomplex sound comprises a consonant, a consonant-to-vowel transition,and optionally a vowel, wherein the consonant is an obstruent stopconsonant and the vowel is a low, back vowel. In other embodiments, acomplex sound comprises a speech syllable selected from the groupconsisting of /da/, /pa/, /ka/, /ta/, /ba/, and /ga/. Methods forrecording a brain response to an acoustic stimulus are known in the art,and further detailed in Section I(a)(iii).

In each of the above aspects, the subject may be symptomatic orasymptomatic. For example, a subject may be asymptomatic at testing andre-testing. Alternatively, a subject may be symptomatic at testing andasymptomatic at re-testing. In another example, a subject may besymptomatic at testing and at re-testing, but one or more symptom mayhave improved when re-testing occurs. In another example, a subject maybe asymptomatic at testing and symptomatic re-testing.

In each of the above aspects, the subject may be identified as having anon-penetrating brain injury in step (a) when a value for at least onecomponent of the brain response is anomalous. In step (b), differencesmay only be calculate for those anomalous values, or may be calculatedfor all previously evaluated components. In the latter, components thatdid not change may be used as a control. Anomalous values and responsecomponents are described above in Section II(a), the disclosures ofwhich are hereby incorporated into this section by reference.

In each of the above aspects “determining any differences in the one ormore components” refers to calculating a difference measure. Thedirection of the change in the difference measure, i.e., positive ornegative, indicates whether the change is an indication of improvementor deterioration in the non-penetrating brain injury. For example, anincrease greater than would be expected by chance in response F₀amplitude, F₀ phase consistency, F₀ sharpness, pitch tracking, responseamplitude over the consonant-to-vowel transition, or stimulus-responsecorrelation value indicates improvement, whereas a decrease greater thanwould be expected by chance in response F₀ frequency error or neuraltiming indicates improvement.

In each of the above aspects, “a value for at least one component of thebrain response” includes one, two, three, four, five or more values forone or more components independently selected from the recited groups,wherein at least one component is an aspect of the frequency followingresponse. “A value for at least one component of the brain response”also includes embodiments where brain response values are combinedtogether using a statistical model to produce a new measurement. Therelated disclosures of Section (II)(a) are hereby incorporated into thissection by reference, as are the disclosures related to the variousaspects of the frequency following response.

III. Systems

An illustrative process and system for automatically generating acousticstimuli and processing brain response data to identify non-penetratingbrain injuries in subjects is depicted in FIG. 6-8. In particular, FIGS.6 and 8 illustrate example processes 600 and 800 for generating stimuliand processing brain stem response data to identify or otherwisedetermine non-penetrating brain injuries. FIG. 7 illustrates a computingenvironment and/or computing system 700 that automatically transmitsacoustic stimuli, receives and processes brain response data, andautomatically generates indications of non-penetrating brain injuriesbased on the brain response data. More specifically, FIG. 7 illustratesa computing environment and/or computing system 700 including a servercomputing device 708 operating in conjunction with various otherhardware and/or software components that may be used to perform orotherwise execute the process 600 and process 800.

Referring initially to FIG. 7, the computing environment 700 includes atransducer controller 702 functionally coupled to an acoustic transducer704 and one or more electrodes 706. More specifically, the transducercontroller 702 represents a computing and/or processing device thatdelivers a stimulus to the acoustic transducer 704. Additionally, thetransducer controller 702 may receive and process brainwave signalinformation from the one or more electrodes 706. The transducercontroller 702 may be any suitable stimulus delivery and dataacquisition system, including PC-based stimulus delivery and dataacquisition systems such as those available from Bio-logic SystemsCorporation or Compumedics. The acoustic transducer 704 may be an insertearphone such as the ER-3 insert earphone available from EtymoticResearch, Elk Grove, Ill. The one or more electrodes 706 may be Ag—AgClscalp electrodes, which may be positioned on the test subject from Cz(active) to ipsilateral earlobe (reference) with forehead ground.

The transducer controller 702 may be functionally connected to acomputing device 708 including a memory 710 within which instructionsare retained directing the operation of the computing device 708 forcarrying out the herein described methods and processes (e.g., process600 of FIG. 6 and process 800 of FIG. 8). More specifically, thecomputing device 708 automatically generates a test stimulus signal,communicates the test stimulus signal to the transducer controller 702for generation of an acoustic stimulus that is presented or otherwiseprovided to the test subject via the acoustic transducer 704. Thecomputing device 708 may obtain brain response data via the electrodes706 and the transducer controller 702. The brain response data may bestored within the memory 710 and/or stored or otherwise maintained in adatabase 712.

The computing device 708 may transmit the brain response data to one ormore client devices 714-720. The or more client devices 714-720functionally communicate with the computing device 708 through acommunications network 721, which may be the Internet, an intranet, andEthernet network, a wireline network, a wireless network, and/or anothercommunication network. The one or more client devices 714-720 may be apersonal computer, work station, mobile device, mobile phone, tabletdevice, processor, and/or other processing device capable ofimplementing and/or executing processes, software, applications, etc.,that includes network-enabled devices and/or software, such asuser-interface 718 for communication over the communications network 112(e.g., browsing the internet). Additionally, the one or more clientdevice(s) 714-720 may include one or more processors that processsoftware or other machine-readable instructions and may include a memoryto store the software or other machine-readable instructions and data.

The database 712 may include one or more data structures used to storeddata for analysis of the acquired brain response data. For example, thedatabase 712 may contain one or more data structures containingnormative response data to which the acquired brain response data may becompared to provide comparison data. The database 712 may furthercontain criteria data for evaluating the comparison data for determiningthe existence of a non-penetrating brain injury.

Referring now to FIG. 6, as stated above, FIG. 6 illustrates a process600 for generating and applying a stimulus to a subject. The stimulussound can include any of a variety of real and/or synthetic soundsincluding a frequency sweep over time against a background (e.g., asound including one or more transitions based on rapid changes infrequency over a period of time, a sound including a formant transitionbuilt with complementary background noise, etc.). One example of astimulus, illustrated in the example method of FIG. 2, is aconsonant-vowel combination against background noise.

At block 610, a consonant sound of a first duration is generated. Forexample, a /d/, /g/, /c/, etc., is selected as the consonant sound toform part of the audio stimulus to elicit a response from the subject.

At block 620, a vowel sound of a second duration is generated. Incertain examples, the second duration is longer than the first duration.That is, the vowel sound is played longer in the stimulus than theconsonant sound. For example, an /a/, /i/, /o/, /u/, etc., is selectedas the vowel sound to accompany the /d/, /g/, /c/, etc., selected as theconsonant sound to form part of the audio stimulus to elicit a responsefrom the subject.

At block 630, a speech sound is generated by combining the consonantsound followed by the vowel sound. For example, the consonant sound andvowel sound are combined by placing the vowel sound after the consonantsound to form the speech sound to be provided in the stimulus. In otherexamples, the consonant sound follows the vowel sound to form the speechsound.

At block 640, the stimulus is generated by mixing a background noisewith the speech sound to generate the stimulus. For example, thebackground noise includes a plurality of voices talking at the same timeand/or approximately the same time to create a human background noiseover which the stimulus can be played. In certain examples, thebackground noise is of a third duration which is longer than the secondduration (and, therefore, also longer than the first duration).

At block 650, the stimulus is provided for output with respect to thesubject. For example, the stimulus can be output as a six-formant stopconsonant constructed in a synthesizer, such as a Klatt-basedsynthesizer at 20 kHz, etc. In certain examples, following an initialstop burst, a consonant transition (e.g., 50 ms from /d/ to /a/, etc.)during which lower formants (e.g., the lower three formants) shift infrequency (e.g., F1 400-720 Hz, F2 1700-1240 Hz, F3 2580-2500 Hz, etc.).In these examples, the lower three formants are steady for thesubsequent vowel (e.g., 120 ms at /a/), and the fundamental frequencyand upper three formants are steady through the stimulus (e.g., F0 100Hz, F4 3300 Hz, F5 3750 Hz, F6 4900 Hz, etc.). The stimulus is presentedagainst a noise or “babble” track (e.g., six voices speakingsemantically anomalous English sentences at a +10 SNR, etc.). In certainexamples, the babble track loops continuously since there is no phasesynchrony between the onsets of the speech sound and the noise. Incertain examples, the stimulus formed from the speech sound and noise ismixed into a single channel that is presented to a single ear of thesubject (e.g., the right ear of the subject at 80 dB of sound pressurelevel (SPL) in alternating polarities throughelectromagnetically-shielded insert earphones, etc.). In certainexamples, stimulus presentation can be controlled with a definedinterstimulus interval (e.g., 61 ms, 81 ms, etc.) in a plurality ofsweeps (e.g., 4200 sweeps, 6300 sweeps, etc.). While the process 600described above describes a specific a complex sound that contains aconsonant to vowel transition, it is contemplated that other complexsounds may be used, such as the complex sounds described above inSection I(a)(i) and Section (a)(iv) above.

Referring now to FIG. 8, a process 800 for analyzing a response to astimulus from one or more subjects is provided. At block 810, acharacteristic waveform definition is extracted from the receivedresponse. For example, a time-locked average of one or more subjectresponses (e.g., inter-response and intra-response averaging) iscomputed to amplify common features and reduce noise to increasesignal-to-noise ratio (SNR) of the characteristic waveform.

At block 820, the characteristic waveform of the response is processedto identify distinct regions within the response. For example, aconsonant-vowel complex sound includes three regions: a) a consonantsound region, b) a transition region between the consonant and thevowel, and c) a vowel sound region. These regions may be the same lengthand/or may be of varying lengths with respect to each other. Forexample, the vowel sound region may be of longer duration than theconsonant sound region, and the transition region may be shorter thanthe consonant sound region.

The vowel region is readily identified by analyzing an end of theresponse to identify a series of evenly spaced peaks that are thebrain's response to the fundamental frequency of the vowel sound. Usingpeak finding techniques such as a windowed, filtered, maxima and/orminima, etc., peaks can be identified and compared for consistency oftemporal spacing. Additionally, this technique can be informed bya-priori knowledge about the fundamental frequency of a sound so that anexpected spacing between the peaks is known. The vowel region is thendefined as the temporal region between the first occurring peak in thistrain of peaks and the end of the response.

The consonant region (e.g., a region of the first onset peak for thestimulus) can be identified using similar peak finding techniques asthose used to find the vowel region. The consonant region is defined asa region between the first large peak, known as the onset peak, in thecharacteristic waveform, and the next peak that exceeds the onset peak'samplitude. The location of both peaks can be further informed by thea-priori knowledge of the stimulus timing and experiential knowledge ofa brain's latency in response to onset of sound stimuli.

Once the consonant and vowel regions have been defined, the transitionregion is defined as the response in temporal period between the end ofthe consonant region and the beginning of the vowel region. Peaks withinthis region can also be identified using the same windowed peak-pickingalgorithm used in identifying peaks in the other two regions.

At block 830, one or more peaks are identified within the determinedregions of the response. For example, peaks can be identified within avowel response region. Using information about the temporal location ofpeaks within the vowel region from the characteristic response as atemplate, peak searching can be seeded within the same region onindividual responses to the same stimulus. By allowing the peak searchto shift slightly within a range relative to the expected location,individual differences in temporal latency from the characteristicresponse can be captured and used for subsequent analysis. Similarly,individual differences in peak location with the transition region maybe captured and used for subsequent analysis.

At block 840, parameters are evaluated based on the regions anddetermined peak information. For example, by analyzing the response toidentify various aspects of the response (e.g., regions of the response,peaks within each region, etc.), parameters (e.g., cABR parameters) canbe evaluated to build a model for determination of the behavioraloutcome of interest. In certain examples, parameters can be added and/orremoved and tested with respect to the developing model. If theparameter improves the model fit, the parameter can be associated withthe model. If, however, the parameter worsens or otherwise fails toimprove the model fit, the parameter is not associated with the model.

In certain examples, one or more databases and/or other data storesinclude data and results from testing of different cABR parameters ondifferent demographics. Databases and/or data stores can also includeindustry-standard behavioral test results obtained from subjects ofvarious ages for comparison in building and evaluating a model.

At block 850, a best fit of available parameters is determined for adesired behavioral outcome model. For example, in determining a bestfit, there are many processes by which a combination of independentvariables (or features) can be derived so that combination best predictsa set of dependent variables (outcome measures) across a population ofindividuals. One such method is regression ((e.g., general linear modelssuch as hierarchical regression, logistic regression, ordinary leastsquares regression, etc.) but other methods include neural networks,latent variable modeling, support vector machines, genetic expressionprogramming, etc. A combination of those independent variables that bestpredicts the values of the outcome measures can be considered apredictive model of those outcome measures (also referred to asbehavioral outcomes) for a population (e.g., for individuals in thatpopulation), given a population that is appropriately-large for thechosen statistical approach. In certain examples, combinations ofindependent variables can be linear combinations and/or non-linearcombinations. Additionally, as discussed above, some variables mayprovide no substantive contribution to the model and may be discarded tosimplify the model's complexity. One process, known as LASSO (LeastAbsolute Shrinkage and Selection Operator) analysis, is a regressionanalysis method that performs variable selection and regularization togenerate a desired model at varying degrees of complexity (e.g., withmore/less independent variables contributing). Resulting selectedparameters can be calculated and used to generate the desired behavioraloutcome model, for example. While the process 800 described abovedescribes a specific a complex sound that contains a consonant to voweltransition, it is contemplated that other complex sounds may be used,such as the complex sounds described above in Section I(a)(i) andSection (a)(iv) above.

FIG. 9 illustrates an example of a suitable computing and networkingenvironment 900 that may be used to implement various aspects of thepresent disclosure described in FIGS. 6 and 7 (e.g. the computing device702 and corresponding components). As illustrated, the computing andnetworking environment 900 includes a general purpose computing device900, although it is contemplated that the networking environment 900 mayinclude other computing systems, such as personal computers, servercomputers, hand-held or laptop devices, tablet devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronic devices, network PCs, minicomputers, mainframecomputers, digital signal processors, state machines, logic circuitries,distributed computing environments that include any of the abovecomputing systems or devices, and the like.

Components of the computer 900 may include various hardware components,such as a processing unit 902, a data storage 904 (e.g., a systemmemory), and a system bus 906 that couples various system components ofthe computer 900 to the processing unit 902. The system bus 906 may beany of several types of bus structures including a memory bus or memorycontroller, a peripheral bus, and a local bus using any of a variety ofbus architectures. For example, such architectures may include IndustryStandard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus,Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA)local bus, and Peripheral Component Interconnect (PCI) bus also known asMezzanine bus.

The computer 900 may further include a variety of computer-readablemedia 908 that includes removable/non-removable media andvolatile/nonvolatile media, but excludes transitory propagated signals.Computer-readable media 908 may also include computer storage media andcommunication media. Computer storage media includesremovable/non-removable media and volatile/nonvolatile media implementedin any method or technology for storage of information, such ascomputer-readable instructions, data structures, program modules orother data, such as RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disks (DVD) or other optical diskstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or any other medium that may be used tostore the desired information/data and which may be accessed by thecomputer 900. Communication media includes computer-readableinstructions, data structures, program modules or other data in amodulated data signal such as a carrier wave or other transportmechanism and includes any information delivery media. The term“modulated data signal” means a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. For example, communication media may include wired mediasuch as a wired network or direct-wired connection and wireless mediasuch as acoustic, RF, infrared, and/or other wireless media, or somecombination thereof. Computer-readable media may be embodied as acomputer program product, such as software stored on computer storagemedia.

The data storage or system memory 904 includes computer storage media inthe form of volatile/nonvolatile memory such as read only memory (ROM)and random access memory (RAM). A basic input/output system (BIOS),containing the basic routines that help to transfer information betweenelements within the computer 900 (e.g., during start-up) is typicallystored in ROM. RAM typically contains data and/or program modules thatare immediately accessible to and/or presently being operated on byprocessing unit 902. For example, in one embodiment, data storage 904holds an operating system, application programs, and other programmodules and program data.

Data storage 904 may also include other removable/non-removable,volatile/nonvolatile computer storage media. For example, data storage904 may be: a hard disk drive that reads from or writes tonon-removable, nonvolatile magnetic media; a magnetic disk drive thatreads from or writes to a removable, nonvolatile magnetic disk; and/oran optical disk drive that reads from or writes to a removable,nonvolatile optical disk such as a CD-ROM or other optical media. Otherremovable/non-removable, volatile/nonvolatile computer storage media mayinclude magnetic tape cassettes, flash memory cards, digital versatiledisks, digital video tape, solid state RAM, solid state ROM, and thelike. The drives and their associated computer storage media, describedabove and illustrated in FIG. 9, provide storage of computer-readableinstructions, data structures, program modules and other data for thecomputer 900.

A user may enter commands and information through a user interface 910or other input devices such as a tablet, electronic digitizer, amicrophone, keyboard, and/or pointing device, commonly referred to asmouse, trackball or touch pad. Other input devices may include ajoystick, game pad, satellite dish, scanner, or the like. Additionally,voice inputs, gesture inputs (e.g., via hands or fingers), or othernatural user interfaces may also be used with the appropriate inputdevices, such as a microphone, camera, tablet, touch pad, glove, orother sensor. These and other input devices are often connected to theprocessing unit 902 through a user interface 910 that is coupled to thesystem bus 906, but may be connected by other interface and busstructures, such as a parallel port, game port or a universal serial bus(USB). A monitor 912 or other type of display device is also connectedto the system bus 906 via an interface, such as a video interface. Themonitor 912 may also be integrated with a touch-screen panel or thelike.

The computer 900 may operate in a networked or cloud-computingenvironment using logical connections of a network interface or adapter914 to one or more remote devices, such as a remote computer. The remotecomputer may be a personal computer, a server, a router, a network PC, apeer device or other common network node, and typically includes many orall of the elements described above relative to the computer 900. Thelogical connections depicted in FIG. 9 include one or more local areanetworks (LAN) and one or more wide area networks (WAN), but may alsoinclude other networks. Such networking environments are commonplace inoffices, enterprise-wide computer networks, intranets and the Internet.

When used in a networked or cloud-computing environment, the computer900 may be connected to a public and/or private network through thenetwork interface or adapter 914. In such embodiments, a modem or othermeans for establishing communications over the network is connected tothe system bus 906 via the network interface or adapter 914 or otherappropriate mechanism. A wireless networking component including aninterface and antenna may be coupled through a suitable device such asan access point or peer computer to a network. In a networkedenvironment, program modules depicted relative to the computer 900, orportions thereof, may be stored in the remote memory storage device.

EXAMPLES

The following examples illustrate various iterations of the invention.However, those of skill in the art should, in light of the presentdisclosure, appreciate that many changes can be made in the specificembodiments which are disclosed and still obtain a like or similarresult without departing from the spirit and scope of the invention.

Example 1 Methods for Examples 2 to 8

The following methods were used for examples 2 to 8.

Experimental Design & Subjects. Two groups of children participated inthis study. Inclusionary criteria included normal hearing, no neurologicdisease, and no history of severe traumatic brain injury (TBI).Descriptive statistics for both groups are provided in Table 1.

The concussion group (N=20, 6 males, mean age=13.39 yr, SD=1.79 yr) wasrecruited from the Institute of Sports Medicine at Ann & Robert H. LurieChildren's Hospital of Chicago, a specialty clinic. Children in thisgroup met clinical diagnostic criteria for a concussion⁶ andparticipated in the experiment following their medical evaluation by asports medicine physician (CRL) with expertise in concussion diagnosisand management. On average, they were evaluated 27 days after theirinjury (mean=26.7 days, SD, 15.3 days, range: 6-56 days). Injuries wereattributed to basketball (N=1), cheerleading (N=2), football (N=3),hockey (N=2), soccer (N=1), softball (N=2), volleyball (N=1), and otherrecreational activities (N=8). Thirteen of the children reported ahistory of a previous concussion. Six of the children had a computerizedtomography (CT) scan of the head and two had magnetic resonance imagingscans of the head; all were normal, except one had a preexisting cystand one child had a slight odontoid asymmetry. Because the concussiongroup was recruited and tested at a specialty clinic the data collectioncould not be blinded to subject group.

A subset of the subjects from the concussion group returned to theclinic for follow-up and were retested (N=11, 3 males, mean age=13.25yr, SD 1.93 yr; average test-retest interval: 34.9 days, SD 15.7 days,range 11-56 days). Although only one of the subjects was fully recoveredat this visit, they were seen by the clinical team as a part ofconcussion monitoring. Two of them reported that their symptoms hadabated since the first evaluation, and at the second evaluation one wascleared to resume normal activities.

The control group (N=20, 6 males, mean age=13.64 yr, SD 1.87 yr) wasrecruited from the community through in-school flyers and word of mouth;none reported a history of brain injury. The groups were matched withrespect to age (t(38)=0.092, p=0.927, Cohen's d=0.029) and had the samedistribution of males and females.

All the subjects passed a hearing screening involving distortion productotoacoustic emission screening, suggesting normal outer hair cellfunction in the cochlea (>6 dB signal-to-noise ratio from 0.4-5 kHz).All subjects had normal auditory brain responses (ABR) to a 100 μs clickpresented at 80.4 dB SPL to the right ear, and the two groups hadsimilar ABR onset response timing (Wave V: t(38)=0.261, p=0.795, Cohen'sd=0.083). The comparable click-evoked response timing suggests thatconcussions do not compromise signal transduction through the peripheralauditory pathway; this observation is consistent with previousresearch¹.

Neurophysiology. Frequency-following responses (FFRs²) were elicited bya 40 msec sound /d/ synthesized in a Klatt-based synthesizer (SenSyn,Sensimetrics Corporation, Malden, Mass.). The stimulus begins with aplosive burst during the first 10 msec, with a 5 msec voice-onset time.During the voiced period of the stimulus, the fundamental frequency (F₀)rises linearly from 103→125 Hz while the formants shift linearly asfollows: F₁ 220→720 Hz, F₂ 1700→1240 Hz, and F₃ 2580→2500 Hz. The lasttwo formants are steady throughout the stimulus (F₄ 3600 Hz, F₅ ₄₅₀₀Hz). Although the stimulus is brief and there is no vowel, it isperceived as the consonant-vowel (CV) syllable /da/. Normative data areavailable for responses to this stimulus in normal-hearing individualsfrom birth to age 72 yr³. Stimuli were delivered and responses werecollected through a Bio-logic Navigator Pro System (Natus Medical Inc.,Mundelein, Ill.). FFRs were measured in a vertical montage with threeAg—AgCl electrodes (Cz active, Fpz ground, right earlobe reference).Stimuli were delivered to the right ear in alternating polarities at80.4 dB sound pressure level at 10.9 Hz through anelectromagnetically-shielded insert earphone (Etymōtic Research, ElkGrove Village, Ill.). Responses were filtered online from 100-2000 Hz(second-order Butterworth) and sampled at 12 kHz. Online artifactrejection was employed at ±23 μV, and two blocks of 3000 artifact-freestimulus presentations were averaged with a 75 msec recording epoch(including a 15.8 msec non-stimulus period, which served as a controlmeasure of background noise).

Concussion Symptom Severity. Children in the concussed group completedthe Postconcussion Symptom Scale (PCSS^(7,8)) to report their symptomload. For each of the 19 symptoms, encompassing neurocognitive,emotional, and somatic aspects of concussion symptomology, subjectsindicated on a Likert scale of 0-6 the intensity of each symptom. ThePCSS total score is the sum of the scores for each symptom, andrepresents the subject's symptom load. Higher scores reflect greatersymptom loads. Total PCSS scores at test one ranged from 0 to 71 (mean31.5, SD 21.9) and, in the eleven children who returned for a follow-uptest, from 0 to 52 at test two (mean 12.8, SD 15.2). Headache was themost frequently reported symptom (test one: 17/20 concussion patients;follow-up test: 7/11 patients). Also reported were difficultyconcentrating (test one: 16/20 concussion patients; follow-up test: 7/11patients), drowsiness (test one: 14/20 concussion patients), andphotosensitivity (test one: 14/20). No patients reported nausea orvomiting.

Data Analyses. Neurophysiological responses were analyzed with respectto amplitude, timing, accuracy, and F₀/pitch processing^(2,3). Thefundamental frequency (F₀) amplitude was defined as the spectralamplitude between 75 to 175 Hz, which corresponds to the F₀ of thestimulus; this was compared to harmonic coding (175-750 Hz). Todetermine spectral amplitudes, the response was converted to thefrequency domain (from 19.5 to 44.2 msec; fast Fourier transformationwith a 2 msec Hanning window). As a complementary analysis to determinethe strength of pitch coding, an autocorrelation was run from 19.5 to44.5 msec (sliding window, 20 msec bins, 1 msec of overlap) and the meancorrelation at a lag corresponding to the average period of the stimuluswas determined⁵. To determine response amplitude over theconsonant-vowel transition, the root-mean-squared amplitude of theresponse was computed from 19.5 to 44.2 msec (corresponding to thevoiced period of the stimulus). To determine timing, the latencies ofseveral stereotyped response peaks were identified. Peaks wereidentified on the final average in consultation with a normativetemplate and two subaverages. To determine accuracy, the stimulus wasfiltered to match the response (100-2000 Hz, second-order Butterworth)and each child's response was cross-correlated to the filtered stimulus(from 19.5 to 44.2 msec; the maximum correlation at an appropriate lagwas obtained and Fisher-transformed to z scores for statisticalpurposes). All statistics reported reflect two-tailed tests.

Example 2 Neural Coding of the Fundamental Frequency is DisruptedFollowing Concussion

The children who sustained a concussion had smaller responses to the F₀than their peers in the control group (by ≈35%), but the groups hadsimilar harmonic processing (group×frequency interaction, F(1,38)=16.554, p<0.001, η² =0.303; post-hoc group differences for F₀,t(38)=3.607, p=0.001, Cohen's d=1.223; harmonics, t(38)=1.056, p=0.298,Cohen's d=0.329; FIG. 2A/B). To complement this analysis, the strengthof pitch coding was determined by performing autocorrelations on theFFRs⁵. Children with a concussion had poorer pitch coding than theirpeers from the control group (t(38)=2.773, p=0.009, Cohen's d=1.14; seeTable 1). Within the concussion group, children who reported the highestsymptom load had the smallest responses to the F₀ (regressioncontrolling for sex, R²=0.548, F(2, 19)=10.287, p=0.001; β_(F0)=−0.712,p=0.001).

TABLE 1 Descriptive statistics for the concussion and control groups.Means are reported with standard deviations. Control ConcussionConcussion-Retest (N = 20) (N = 20) (N = 11) Age (yr) 13.64 (1.87) 13.69 (1.79)  13.25 (1.29)  Male:Female 6:14 6:14 3:8 Click V Latency5.64 (0.22) 5.66 (0.22)  5.66 (0.30) (msec) Amplitude over CV 0.09(0.02) 0.07 (0.03)* 0.07 (0.04) Transition (μV) Timing (msec) V 6.52(0.25) 6.62 (0.24)  6.82 (0.41) A 7.42 (0.27) 7.65 (0.31)* 7.82 (0.41) D22.29 (0.30)  22.63 (0.59)*  22.80 (0.92)  E 30.80 (0.40)  31.23(0.44)** 31.09 (0.30)  F 39.37 (0.35)  39.52 (0.44)  39.55 (0.52)  O48.24 (0.37)  48.13 (0.38)  48.18 (0.41)  Stimulus-response correlation0.15 (0.10) 0.08 (0.04)* 0.08 (0.05) (Pearson's r) Spectral amplitude F₀0.068 (0.017)  0.048 (0.019)***  0.062 (0.015)^(†) (μV) Harmonics 0.019(0.005) 0.017 (0.007)  0.018 (0.004) Pitch coding(Pearson's r) 0.30(0.08)  0.24 (0.07)** 0.24 (0.09) Concussion vs. Control group: *p <0.05, **p < 0.01, ***p = 0.001; Concussion Subgroup Test 1 vs. Test 2^(†)p < 0.05.

Example 3 Diminished Neural Responses to Speech Following Concussion

The next analysis considered the amplitude of the neural response overthe CV transition. Children who sustained a concussion had smallerresponses to speech than their uninjured peers (t(38)=2.382, p=0.022,Cohen's d=0.832; FIG. 3A/D). Because the response to the F₀ dominatesthe time-domain FFR, this may be a corollary of the diminished F₀.

Example 4 Slower Neural Responses to Speech Following Concussion

The next analysis considered the timing of neural processing by askinghow quickly the auditory system responds to several cues in the speechsound, which are represented by characteristic peaks in the FFR. Thechildren who sustained a concussion had slower responses to some, butnot all, stimulus features (group×peak interaction, F(5, 30)=5.091,p=0.002, η² =0.459; FIG. 3A). Post-hoc tests comparing individual peaksshowed that children in the concussion group had responses nearly 0.4msec slower than their uninjured peers for three of the six responsepeaks, reflecting the coding of the periodicity (note not all peaks weredetectable in every child; Peak A, t(38)=2.542, p=0.015, Cohen'sd=0.804;Peak D, t(36)=2.258, p=0.030, Cohen's d=746; Peak E,t(37)=3.301, p=0.002, Cohen's d=1.059; FIG. 3A). While a timingdiscrepancy of 0.4 msec is small, in the context of the subcorticalauditory system it is clinically significant. These particular peaksreflect the coding of the F₀ ⁴, suggesting that both the amplitude andthe timing of F₀-coding is disrupted.

However, the two groups had similar timing to the onset of the sound(Peak V, t(32)=1.358, p=0.183, Cohen's d=0.429), for the last peakreflecting the transition into a steady state vowel (Peak F,t(38)=1.169, p=0.250, Cohen's d=0.370), and in response to the offset ofthe sound (Peak O, t(36)=0.876, p=0.387, Cohen's d=0.284; FIG. 3A).Thus, it appears concussions impart a selective timing delay that onlyaffects the neural coding of certain speech features. Specifically, itappears concussions target the coding of periodicity (F₀) cues in speechwhile sparing transients (such as plosive onset bursts). Eachindividual's FFR was correlated to the stimulus to achieve a “global”measure of the integrity of neural processing. Children in theconcussion group, on average, had less accurate neural coding of thespeech sound than their uninjured peers (t(38)=2.660, p=0.011, Cohen'sd=0.841).

Example 5 Less Accurate Neural Responses to Speech Following Concussion

Each individual's FFR was correlated to the stimulus to achieve a“global” measure of the integrity of neural processing. Children in theconcussion group, on average, had less accurate neural coding of thespeech sound than their uninjured peers (t(38)=2.660, p=0.011, Cohen'sd=0.841).

Example 6 A Biomarker of Concussion

The preceding analyses validated the predictions that (1) children witha concussion have poorer neural processing of sound than their peers,(2) this profile is grounded in neural processing of the F₀, and (3) theintegrity of this processing relates to the severity of the injury.Next, it was hypothesized that these physiological measures could becombined to classify children into concussion and control groups. If so,certain FFR properties could be used in aggregate as a biological markerto objectively and reliably identify a concussion.

A binary logistic regression was conducted, which asks how a series ofmeasures combine to predict group membership. While the previoussections defined the specific neural functions that are disrupted inchildren with a concussion, how the FFR distinguishes betweenindividuals with and without these injuries was evaluated in theseexperiments. A particular goal was to determine if these objectivebiological factors could, in combination, identify the children in thisstudy who had sustained a concussion. A two-step model was used that, onthe first step, incorporated subject age, the background noise in theFFR (amplitude of non-stimulus-related neural activity), and the timingof the onset response to sound (wave V in response to a click). Thesecond step incorporated (singly and in various combinations) theamplitude of the response to the F₀, the size of the onset response(defined here as the area between Peaks V and A), the accuracy ofencoding the speech sound (stimulus-response correlation), and responseamplitude over the harmonic coding region (defined here as 175-750 Hz).When the second step of the model included amplitude of the response tothe F₀, size of the onset response, and the stimulus-responsecorrelation, the model correctly classified subjects into concussion orcontrol groups as shown in Table 2 (Log likelihood ratio=23.028,Nagelkerke R2=0.741, X²(6)=32.423, p<0.001). The percentage of subjectsaccurately classified varied depending upon response components selectedfor the second step.

TABLE 2 A binary logistic regression that incorporates multiple aspectsof auditory-neurophysiological processing reliably classifies 90% ofchildren into concussion or control groups. B S.E. Wald χ² Step 1 Age−0.24 0.33 0.52 Prestimulus amplitude −11.26 63.67 0.03 Wave V ABRlatency 2.545 2.99 0.72 Step 2 CV onset amplitude 40.14 17.54 5.33* F₀amplitude −161.82 59.74 7.34* Stimulus-response correlation −20.83 8.635.82** *p < 0.05, **p = 0.01.

Finally, the predictive utility of the model described in Table 2 wasevaluated by conducting a receiver operating characteristic (ROC)analysis on scores from the logistic regression. A cut-off of 0.596 onthe regression score was found to achieve a 90% sensitivity (truepositive rate; 18 out of 20 mTBI subjects correctly classified) and a95% specificity (true negative rate; 19 out of 20 control subjectscorrectly classified) was an excellent fit for the data (area under thecurve=0.945, p<0.001, 95% confidence interval 0.875-1.000; see FIG. 10).These correspond to a 94.7% positive predictive value (PPV, probabilitythat a positive is true) and a 90.4% negative predictive value (NPV,probability that a negative is true). For comparison, the ImPACT—awidely-used, behavioral test battery—has an 89.4$ PPV and 81.9% NPV.¹¹Similarly, the Standardized Assessment of Concussions has a 91.2% PPVand an 83.1% NPV.¹²

Example 7 Partial Recovery of Neural Processing as Concussion SymptomsAbate

The final analysis focused on children who returned to the clinic for asecond evaluation. If sound processing is disrupted by a concussion,then it follows that this processing should improve through the courseof recovery. At the second test, all of the children reported areduction in their symptom loads, suggesting that they were on the roadto recovery (subgroup only; PCSS: Test 1, mean 37.1, SD 22.5; Test 2,mean 12.8, SD 15.2; t(10)=4.342, p=0.002). It is important to note,however, that only one of the children was clinically determined to becompletely recovered from the concussion at this second visit andcleared to resume normal activities. In line with this reduction insymptom load, F₀ responses were found to be ˜30% larger at the secondtest, whereas responses to the harmonics remained the same(test×frequency interaction, F(1, 10)=6.287, p=0.031, η²=0.386; F₀:t(10)=2.397, p=0.037; harmonics: t(10)=1.453, p=0.177). As illustratedin FIG. 4B, the re-test group's F₀ amplitude matched the range of thecontrol group. The minimal detectable change in F₀ amplitude based onpublished norms⁹ was also computed; this provides a cutoff for a changein F₀ amplitude that would be more than expected by chance¹⁰. 6 of the11 children in this group improved in F₀ amplitude beyond chance (changeof 0.006 μV). As shown in FIG. 4C, of the five that did notsignificantly increase in F₀ amplitude, none declined significantly.While this is a small subsample, this longitudinal evidence for F₀recovery provides a converging proof-of-concept that reinforces thecross-sectional findings.

Example 8 Detection of Lasting Neurological Damage in Subjects With aPrevious Non-Penetrating Brain Injury

Twenty-five male student-athletes with a history of one concussion(11-82 months before participation; mean=36.1, SD=20.0) were recruitedfrom a college football team. All were healthy and active at the time oftesting. Controls were twenty-five age- and position-matched teammateswho reported no previous concussions. Frequency-following responses(FFRs) to speech, an electrophysiological response from the auditorymidbrain that depends on synchronous neural firing and reflects auditorypathway health with microsecond precision, provided our outcome measure.The approach used is substantially similar to methods outlined inExample 1.

Student-athletes with and without a previous concussion respondeddistinctly to the F₀ and harmonics (FIG. 5A; group×frequencyinteraction: F(1,48)=6.012, p=0.018, η²=0.111). Student-athletes withone previous concussion had smaller F0 responses than those without(t(48)=2.251, p=0.029, Cohen's d=0.918 ; Concussion mean (SD)=0.0467 μV(0.0014), 95% CI=[0.0407, 0.0527]; No Concussion mean (SD)=0.0557 μV(0.0138), 95% CI=[0.0500, 0.0613]). These groups had similar responsesto harmonics (t(48)=0.066, p=0.947, Cohen's d=0.021; Concussion mean(SD)=0.0155 μV (0.0060), 95% CI=[0.013, 0.018]; No concussion mean(SD)=0.0154 μV (0.0031), 95% CI=[0.013, 0.017]).

As a whole, the student-athletes had F₀ responses below the 50^(th)percentile for this age group⁹ (FIG. 5B; one-sample t-test: t(49)=4.507,p <0.001, mean percentile=28.4, 95% CI=[20.6, 37.8]). On average, thegroup without a previous concussion had F₀s at the 38.5^(th) percentile(t(24)=1.837, p=0.079, 95% CI=[25.6, 51.4]) whereas the group with aprevious concussion had F₀s at the 19.8^(th) percentile (t(24)=4.628, p<0.001, 95% CI=[11.0, 31.9]).

Overall, student-athletes with a prior concussion had smaller responsesto the F₀ of speech than their teammates who never experienced aconcussion. The putative legacy of this injury was evident despiteindications that student-athletes had recovered. This neural hallmark ofa previous concussion manifests similarly—albeit more mildly—as thatobserved in younger, symptomatic concussed student-athletes.

References for the Examples

1. Gallun, F. J. et al. Performance on tests of central auditoryprocessing by individuals exposed to high-intensity blasts. J. Rehabil.Res. Dev. 49, 1005 (2012).

2. Skoe, E. & Kraus, N. Auditory brain stem response to complex sounds:A tutorial. Ear Hear. 31, 302-324 (2010).

3. Skoe, E., Krizman, J., Anderson, S. & Kraus, N. Stability andplasticity of auditory brainstem function across the lifespan. Cereb.Cortex 25, 1415-1426 (2015).

4. Kraus, N. & Nicol, T. Brainstem origins for cortical ‘what’ and‘where’ pathways in the auditory system. Trends Neurosci. 28, 176-181(2005).

5. Carcagno, S. & Plack, C. J. Subcortical plasticity followingperceptual learning in a pitch discrimination task. JARO-J. Assoc. Res.Otolaryngol. 12, 89-100 (2011).

6. McCrory, P. et al. Consensus statement on concussion in sport: the4th International Conference on Concussion in Sport held in Zurich,November 2012. Br. J. Sports Med. 47, 250-258 (2013).

7. Kontos, A. P. et al. A revised factor structure for thePost-Concussion Symptom Scale baseline and postconcussion factors. Am.J. Sports Med. 40, 2375-2384 (2012).

8. Joyce, A. S., LaBella, C. R., Carl, R. L., Lai, J.-S. & Zelko, F. A.The Postconcussion Symptom Scale: Utility of a three-factor structure.Med. Sci. Sports Exerc. 47, 1119-1123 (2015).

9. Skoe E, Krizman J, Anderson S, Kraus N. Stability and plasticity ofauditory brainstem function across the lifespan. Cereb Cortex. 25,1415-1426 (2015).

10. Vander Roer et al. Minimal clinically important change for painintensity, functional status, and general health status in patients withnonspecific low back pain. Spine 31: 578-582 (2006).

11. Schartz et al. Arch. Clin. Neuropsychol. 21, 91-99 (2006).

12. Barr et al. J. Int. Neuropsychol. Soc. 7, 693-702 (2001).

1. A method of identifying non-penetrating brain injury in a subject, the method comprising: (a) fitting the subject with electrodes to measure voltage potentials generated from the subject's brain; (b) administering to the subject an acoustic stimulus, wherein the acoustic stimulus is comprised of a complex sound, and the complex sound comprises a consonant, a consonant-to-vowel transition, and optionally a vowel; (c) recording voltage potentials from the subject's brain for at least the duration of the acoustic stimulus; (d) analyzing the voltage potentials to determine one or more components of the brain response; and (e) identifying the subject as having a non-penetrating brain injury when a value for at least one component of the brain response is anomalous; wherein the components are selected from the group consisting of fundamental frequency (F₀), neural timing of a sustained response peak, response amplitude over a time window that comprises some or all of the consonant-vowel transition, and stimulus-response correlation over a time window that encompasses some or all of the consonant-vowel transition.
 2. A method of identifying non-penetrating brain injury in a subject, the method comprising: (a) analyzing one or more components of the subject's brain response to an acoustic stimulus, wherein the acoustic stimulus is comprised of a complex sound, and the complex sound comprises a consonant, a consonant-to-vowel transition, and optionally a vowel; and (b) identifying the subject as having a non-penetrating brain injury when a value for at least one component of the brain response is anomalous, wherein the components are selected from the group consisting of fundamental frequency (F₀), neural timing of a sustained response peak, response amplitude over a time window that comprises some or all of the consonant-vowel transition, and stimulus-response correlation over a time window that encompasses some or all of the consonant-vowel transition.
 3. A method for classifying a subject that received a hit to the body that transmitted an impulsive force to the brain, the method comprising: (a) fitting the subject with electrodes to measure voltage potentials generated from the subject's brain; (b) administering to the subject an acoustic stimulus, wherein the acoustic stimulus is comprised of a complex sound, and the complex sound comprises a consonant, a consonant-to-vowel transition, and optionally a vowel; (c) recording voltage potentials from the subject's brain for at least the duration of the acoustic stimulus; (d) analyzing the voltage potentials to determine one or more components of the brain response; and (e) classifying the subject as having a non-penetrating brain injury when a value for at least one component of the brain response is anomalous; wherein the components are selected from the group consisting of fundamental frequency (F₀), neural timing of a sustained response peak, response amplitude over a time window that comprises some or all of a consonant-vowel transition, and stimulus-response correlation over a time window that encompasses some or all of a consonant-vowel transition.
 4. A method for classifying a subject that received a hit to the body that transmitted an impulsive force to the brain, the method comprising: (a) analyzing one or more components of the subject's brain response to an acoustic stimulus, wherein the acoustic stimulus is comprised of a complex sound, and the complex sound comprises a consonant, a consonant-to-vowel transition, and optionally a vowel; and (b) classifying the subject as having a non-penetrating brain injury when a value for at least one component of the brain response is anomalous, wherein the component(s) is selected from the group consisting of fundamental frequency (F₀), neural timing of a sustained response peak, response amplitude over the consonant-vowel transition, and stimulus-response correlation over a time window that encompasses the consonant-vowel transition.
 5. The method of any one of the preceding claims, wherein the subject is asymptomatic.
 6. The method of any one of the claims 1 to 4, wherein the subject is symptomatic.
 7. A method for assessing a subject's recovery from a non-penetrating brain injury, the method comprising: (a) testing the subject's brain response to an acoustic stimulus by: (i) fitting the subject with electrodes to measure voltage potentials generated from the subject's brain; (ii) administering to the subject an acoustic stimulus, wherein the acoustic stimulus is comprised of a complex sound, and the complex sound comprises a consonant, a consonant-to-vowel transition, and optionally a vowel; (iii) recording voltage potentials from the subject's brain for at least the duration of the acoustic stimulus; (iv) analyzing the voltage potentials to determine one or more components of the brain response; and (v) identifying a value for at least one component of the brain response that is anomalous; wherein the components are selected from the group consisting of fundamental frequency (F₀), neural timing of a sustained response peak, response amplitude over a time window that comprises some or all of the consonant-vowel transition, and stimulus-response correlation over a time window that encompasses some or all of the consonant-vowel transition; (b) re-testing the subject's brain response to the acoustic stimulus at a later time by repeating steps a(i) to a(iv), and identifying the value for the one or more components that were anomalous in step (a)(v) (“the re-test value”); and (c) calculating the difference between the re-test value and the anomalous value wherein the subject is determined to be recovering from the non-penetrating brain injury when there is a change in the re-test value that is greater than would be expected by chance, and the direction of the change indicates an improvement in the component of the brain response; and wherein the subject is determined to not be recovering from the non-penetrating brain injury when (a) there is not a change in the re-test value that is greater than would be expected by chance, and the direction of the change indicates an improvement in the component of the brain response, or (b) when there is a change in the re-test value that is greater than would be expected by chance, and the direction of the change indicates a deterioration in the component of the brain response.
 8. The method of claim 7, wherein the subject is symptomatic at the time of testing.
 9. The method of claim 8, wherein the subject reports an improvement in symptoms at re-testing.
 10. The method of claim 8, wherein the subject reports no improvement in symptoms at re-testing.
 11. The method of claim 7, wherein the subject is asymptomatic at the time of testing.
 12. The method of any one of the preceding claims, wherein at least one component of the brain response comprises F₀ and the value is F₀ amplitude, F₀ phase consistency, F₀ sharpness, F₀ frequency error, pitch tracking, or any combination thereof.
 13. The method of claim 12, wherein at least one component of the brain response comprises F₀ amplitude.
 14. The method of any one of the preceding claims, wherein at least one component of the brain response comprises neural timing of a sustained response peak.
 15. The method of any one of the preceding claims, wherein at least one component of the brain response comprises response amplitude over a time window that comprises some or all of a consonant-vowel transition.
 16. The method of any one of the preceding claims, wherein at least one component of the brain response comprises stimulus-response correlation over a time window that comprises some or all of a consonant-vowel transition, and the stimulus-response correlation is calculated in the time domain.
 17. The method of any one of the preceding claims, wherein at least one component of the brain response comprises stimulus-response correlation over a time window that comprises some or all of a consonant-vowel transition, and the stimulus-response correlation is calculated in the frequency domain.
 18. The method of any one of claims 15 to 17, wherein the time window comprises at least one formant.
 19. The method of any one of claims 15 to 17, wherein the time window comprises at least two formants.
 20. The method of any one of claims 15 to 17, wherein the time window further comprises the unvoiced consonant release and/or the transient frequency following response component corresponding to the onset of voicing.
 21. The method any one of the preceding claims, wherein the subject is identified as having a non-penetrating brain injury when values for at least two components are anomalous.
 22. The method any one of the preceding claims, wherein the subject is identified as having a non-penetrating brain injury when values for at least three components are anomalous.
 23. The method of any one of claims 1 to 20, wherein a value for at least one component of the brain response is a measurement produced by a statistical model, the statistical model comprising a value for a component selected from the group consisting of fundamental frequency (F₀), neural timing of a sustained response peak, response amplitude over a time window that comprises some or all of the consonant-vowel transition, and stimulus-response correlation over a time window that encompasses some or all of the consonant-vowel transition.
 24. The method of claim 23, wherein the statistical model comprises two or more values, each value for a component selected from the group consisting of fundamental frequency (F₀), neural timing of a sustained response peak, response amplitude over a time window that comprises some or all of the consonant-vowel transition, and stimulus-response correlation over a time window that encompasses some or all of the consonant-vowel transition.
 25. The method of any one of claims 21 to 24, wherein the two components are F₀ and stimulus-response correlation over a time window that encompasses the consonant-vowel transition.
 26. The method of any one of the preceding claims, wherein at least one component of the brain response further comprises onset peak amplitude or onset response amplitude.
 27. The method any one of the preceding claims, wherein the acoustic stimulus further comprises a background noise.
 28. The method of any one of the preceding claims, wherein the acoustic stimulus is repeatedly administered to the subject; voltage potentials are recorded for at least the duration of the repeated acoustic stimuli; and an average response to the acoustic stimulus is produced from the recorded voltage potentials before determining one or more components of the FFR.
 29. The method of any one of the preceding claims, wherein the consonant is an obstruent stop consonant.
 30. The method of any one of the preceding claims, wherein the vowel is a low, back vowel.
 31. The method of any one of the preceding claims, wherein the complex sound comprises a speech syllable selected from the group consisting of /da/, /pa/, /ka/, /ta/, /ba/, and /ga/.
 32. The method of any one of the preceding claims, wherein the complex sound comprises a speech syllable selected from the group consisting of /da/, /pa/, /ka/, /ta/, /ba/, and /ga/, with the provisio that the complex sound is not a word.
 33. The method of any one of the preceding claims, wherein the complex sound consists of a speech syllable selected from the group consisting of /da/, /pa/, /ka/, /ta/, /ba/, and /ga/. 34-42. (cancelled) 